def train(self):
"""
Trains BDT.
Randomly samples a schedule and timestep within that schedule, and passes in the corresponding data in an attempt to classify which task was scheduled
:return:
"""
loss_func = AlphaLoss()
training_done = False
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(len(self.X))
input_nn = self.X[rand_timestep_within_sched]
truth_nn = self.Y[rand_timestep_within_sched]
# iterate over pairwise comparisons
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(
1, 242)).cuda()) # change to 5 to increase batch size
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(
np.asarray(truth_nn).reshape(1)).cuda().long())
else:
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(1, 242)))
P = Variable(
torch.Tensor(np.ones((1, 20) * self.distribution_epsilon)))
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(np.asarray(truth_nn).reshape(1)).long())
self.opt.zero_grad()
output = self.model.forward(input_nn)
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .001 or loss.item() > 30:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)
self.opt.step()
self.total_loss_array.append(loss.item())
total_iterations = len(self.total_loss_array)
if total_iterations % 50 == 49:
print('loss at', total_iterations,
', total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if total_iterations > 25000:
training_done = True
def train(self):
"""
Trains LSTM.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
timesteps = None
training_done = False
loss_func = AlphaLoss()
which_schedule = None
while not training_done:
# sample a timestep before the cutoff for cross_validation
# Quick Fix
found_a_suitable_candidate = False
while not found_a_suitable_candidate:
rand_timestep_within_sched = np.random.randint(len(self.X))
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, rand_timestep_within_sched)
if rand_timestep_within_sched + 2 > self.schedule_array[
which_schedule][1]:
pass
else:
found_a_suitable_candidate = True
timesteps = [
rand_timestep_within_sched,
rand_timestep_within_sched + 1,
rand_timestep_within_sched + 2
]
self.model.reinitialize_hidden_to_random()
load_in_embedding_bnn(self.model, self.embedding_list,
which_schedule)
for timestep in timesteps:
truth = self.Y[timestep]
previous_hidden_state = tuple(
[t.detach().cuda() for t in self.model.hidden])
input_nn = self.X[timestep]
truth_nn = self.Y[timestep]
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(
1,
242)).cuda()) # change to 5 to increase batch size
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(
np.asarray(truth_nn).reshape(1)).cuda().long())
else:
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(1, 242)))
P = Variable(
torch.Tensor(
np.ones((1, 20) * self.distribution_epsilon)))
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(np.asarray(truth_nn).reshape(1)).long())
self.opt.zero_grad()
output = self.model.forward(input_nn, previous_hidden_state)
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .05 or loss.item() > 5:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
self.total_loss_array.append(loss.item())
self.embedding_list = store_embedding_back_bnn(
self.model, self.embedding_list, which_schedule)
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
self.save_trained_nets('lstm_small' + str(self.num_schedules))
if self.total_iterations > 11000 and np.mean(
self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]
) < self.convergence_epsilon:
training_done = True
def train(self):
"""
Trains BDT.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
training_done = False
cv_cutoff = .8 * len(self.start_of_each_set_twenty)
loss_func = AlphaLoss()
# variables to keep track of loss and number of tasks trained over
running_loss_predict_tasks = 0
num_iterations_predict_task = 0
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(cv_cutoff)
set_of_twenty = self.start_of_each_set_twenty[
rand_timestep_within_sched]
# truth is the same as the task chosen
# NOTE: if initial truth > 10, it is biggest task first. Else it is smallest task first. Approximate way to keep track of
# the current type of schedule
truth = self.Y[set_of_twenty]
# get the current schedule that the timestep is from
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, set_of_twenty)
# load in the embedding from the list into the network
load_in_embedding(self.model, self.embedding_list, which_schedule)
# find feature vector of true action taken
phi_i_num = truth + set_of_twenty
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
# transform into torch variables
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)).cuda())
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]))
# get model output
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# Updates embeddings and optimizer after 5000 passes through the data
if self.total_iterations > 5000:
self.opt.zero_grad()
self.embedding_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
self.embedding_optimizer.step()
else:
self.opt.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)).cuda())
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]))
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# Updates embeddings and optimizer after 5000 passes through the data
if self.total_iterations > 5000:
self.opt.zero_grad()
self.embedding_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
self.embedding_optimizer.step()
else:
self.opt.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
# save the embedding back into the list
self.embedding_list = store_embedding_back(self.model,
self.embedding_list,
which_schedule)
# add average accuracy across pairwise comparisons to array
self.total_loss_array.append(running_loss_predict_tasks /
num_iterations_predict_task)
num_iterations_predict_task = 0
running_loss_predict_tasks = 0
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
print(self.embedding_list)
self.save_trained_nets()
if self.total_iterations > 8000 and np.mean(
self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]) < self.covergence_epsilon:
training_done = True
def evaluate(self, load_in_model=False):
"""
Evaluate performance of a trained network tuned upon the alpha divergence loss.
This is tested on 20% of the data and will be stored in a text file.
Note this function is called after training convergence
:return:
"""
# define new optimizer that only optimizes gradient
loss_func = AlphaLoss()
checkpoint = self.model.state_dict().copy()
embedding_list_copy = self.embedding_list.copy()
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
if load_in_model:
self.model.load_state_dict(
torch.load(
'/home/ghost/PycharmProjects/bayesian_prolo/saved_models/pairwise_saved_models/model_baydimtest_'
+ str(self.bayesian_embedding_dim) +
'.tar')['nn_state_dict'])
# step through the cv set
for j in range(int(self.num_schedules * .8), self.num_schedules):
optimizer_for_embedding = self.opt = torch.optim.SGD([{
'params':
self.model.bayesian_embedding,
'lr':
.9
}])
load_in_embedding(self.model, self.embedding_list, j)
schedule_bounds = self.schedule_array[j]
step = schedule_bounds[
0] # starting index of schedule within the data
# until you complete schedule 1
while step < schedule_bounds[1]:
probability_matrix = np.zeros((20, 20))
for m, counter in enumerate(range(step, step + 20)):
phi_i = self.X[counter]
phi_i_numpy = np.asarray(phi_i)
# for each set of twenty
for n, second_counter in enumerate(range(step, step + 20)):
# fill entire array with diagnols set to zero
if second_counter == counter: # same as m = n
continue
phi_j = self.X[second_counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
# push through nets
preference_prob = self.model.forward(feature_input)
probability_matrix[m][n] = preference_prob[
0].data.detach()[0].item()
probability_matrix[n][m] = preference_prob[
0].data.detach()[1].item()
# Set of twenty is completed
column_vec = np.sum(probability_matrix, axis=1)
# top 1
choice = np.argmax(column_vec)
# top 3
_, top_three = torch.topk(torch.Tensor(column_vec), 3)
# Then do training update loop
truth = self.Y[step]
# index top 1
if choice == truth:
prediction_accuracy[0] += 1
# index top 3
if truth in top_three:
prediction_accuracy[1] += 1
# forward
phi_i_num = truth + step # old method: set_of_twenty[0] + truth
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
if choice == truth:
pass
else:
for counter in range(step, step + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
# label = add_noise_pairwise(label, self.noise_percentage)
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(
1, 13)).cuda())
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]))
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# update till convergence, or under 50 iterations
if loss.item() > .005:
flag = False
tracker = 0
while not flag:
optimizer_for_embedding.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
optimizer_for_embedding.step()
output = self.model(feature_input)
loss = loss_func.forward(
P, output, self.alpha)
tracker += 1
if tracker > 50:
flag = True
if loss.item() < .005:
flag = True
else:
pass
for counter in range(step, step + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(
1, 13)).cuda())
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]))
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# update till convergence, or under 50 iterations
if loss.item() > .005:
flag = False
tracker = 0
while not flag:
optimizer_for_embedding.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
optimizer_for_embedding.step()
output = self.model(feature_input)
loss = loss_func.forward(
P, output, self.alpha)
tracker += 1
if tracker > 50:
flag = True
if loss.item() < .005:
flag = True
else:
pass
# add average loss to array
self.embedding_list = store_embedding_back(
self.model, self.embedding_list, j)
step += 20
# schedule finished
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20,
' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', j)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
self.save_performance_results(percentage_accuracy_top1,
percentage_accuracy_top3)
self.model.load_state_dict(checkpoint)
self.embedding_list = embedding_list_copy
def evaluate_alpha(self):
"""
Evaluate performance of a trained network.
This is tested on 20% of the data and will be stored in a text file.
:return:
"""
opt = torch.optim.RMSprop([{
'params': self.model.bayesian_embedding,
'lr': .001
}])
loss_func = AlphaLoss()
percentage_accuracy_top1 = []
for i, schedule in enumerate(self.schedule_array):
if i < .8 * len(self.schedule_array):
continue
load_in_embedding(self.model, self.embedding_list, i)
prediction_accuracy = 0
for count in range(schedule[0], schedule[1] + 1):
input_nn = self.X[count]
truth_nn = self.Y[count]
# if torch.cuda.is_available():
# input_nn = Variable(torch.Tensor(np.asarray(net_input).reshape(1, 242)).cuda())
# truth = Variable(torch.Tensor(np.asarray(truth).reshape(1)).cuda().long())
# else:
# input_nn = Variable(torch.Tensor(np.asarray(net_input).reshape(1, 242)))
# truth = Variable(torch.Tensor(np.asarray(truth).reshape(1)))
# iterate over pairwise comparisons
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(
1,
242)).cuda()) # change to 5 to increase batch size
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(
np.asarray(truth_nn).reshape(1)).cuda().long())
else:
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(1, 242)))
P = Variable(
torch.Tensor(
np.ones((1, 20) * self.distribution_epsilon)))
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(np.asarray(truth_nn).reshape(1)).long())
opt.zero_grad()
output = self.model.forward(input_nn)
loss = loss_func.forward(P, output, self.alpha)
loss.backward()
opt.step()
index = torch.argmax(output).item()
if index == truth.item():
prediction_accuracy += 1
print('Prediction Accuracy: top1: ', prediction_accuracy / 20)
store_embedding_back(self.model, self.embedding_list, i)
print('schedule num:', i)
percentage_accuracy_top1.append(prediction_accuracy / 20)
self.save_performance_results(percentage_accuracy_top1)
print(np.mean(percentage_accuracy_top1))
def train(self):
"""
Trains BDT.
Randomly samples a schedule and timestep within that schedule, and passes in the corresponding data in an attempt to classify which task was scheduled
:return:
"""
criterion = torch.nn.CrossEntropyLoss()
loss_func = AlphaLoss()
training_done = False
cv_cutoff = .8 * len(self.X)
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(cv_cutoff)
input_nn = self.X[rand_timestep_within_sched]
truth_nn = self.Y[rand_timestep_within_sched]
which_schedule = self.find_which_schedule_this_belongs_to(
rand_timestep_within_sched)
load_in_embedding(self.model, self.embedding_list, which_schedule)
load_in_embedding(self.model_NLL, self.embedding_list_NLL,
which_schedule)
# iterate over pairwise comparisons
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(
1, 242)).cuda()) # change to 5 to increase batch size
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(
np.asarray(truth_nn).reshape(1)).cuda().long())
else:
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(1, 242)))
P = Variable(
torch.Tensor(np.ones((1, 20) * self.distribution_epsilon)))
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(np.asarray(truth_nn).reshape(1)).long())
self.opt.zero_grad()
output = self.model.forward(input_nn)
loss = loss_func.forward(P, output, self.alpha)
loss.backward()
self.opt.step()
self.opt2.zero_grad()
output_nn = self.model_NLL.forward(input_nn)
loss_nn = criterion(output_nn, truth)
loss_nn.backward()
self.opt2.step()
self.total_loss_array.append(loss.item())
store_embedding_back(self.model, self.embedding_list,
which_schedule)
store_embedding_back(self.model_NLL, self.embedding_list_NLL,
which_schedule)
total_iterations = len(self.total_loss_array)
if total_iterations % 50 == 49:
print('loss at', total_iterations, 'is', loss.item())
print('loss_NN at', total_iterations, 'is', loss_nn.item())
if total_iterations > 100000:
training_done = True
def train(self):
"""
Trains PDDT.
:return:
"""
threshold = .05
training_done = False
loss_func = AlphaLoss()
# deepening data
deepen_data = {'samples': [], 'labels': [], 'embedding_indices': []}
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(len(self.X))
input_nn = self.X[rand_timestep_within_sched]
truth_nn = self.Y[rand_timestep_within_sched]
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, rand_timestep_within_sched)
load_in_embedding(self.model, self.embedding_list, which_schedule)
deepen_data['samples'].append(np.array(input_nn))
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(
1, 242)).cuda()) # change to 5 to increase batch size
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(
np.asarray(truth_nn).reshape(1)).cuda().long())
else:
input_nn = Variable(
torch.Tensor(np.asarray(input_nn).reshape(1, 242)))
P = Variable(
torch.Tensor(np.ones((1, 20) * self.distribution_epsilon)))
P[0][truth_nn] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(np.asarray(truth_nn).reshape(1)).long())
self.opt.zero_grad()
output = self.model.forward(input_nn)
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .001 or loss.item() > 30:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)
self.opt.step()
deepen_data['labels'].extend([truth.item()])
deepen_data['embedding_indices'].extend([which_schedule])
# add average loss to array
# print(list(self.model.parameters()))
self.embedding_list = store_embedding_back(self.model,
self.embedding_list,
which_schedule)
self.total_loss_array.append(loss.item())
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
self.save_trained_nets('BDDT' + str(self.num_schedules))
threshold -= .1
if self.total_iterations % 500 == 499:
self.model = deepen_with_embeddings(
self.model,
deepen_data,
self.embedding_list,
max_depth=self.max_depth,
threshold=threshold /
len(self.model.leaf_init_information))
params = list(self.model.parameters())
del params[0]
self.opt = torch.optim.RMSprop([{
'params': params
}, {
'params': self.model.bayesian_embedding,
'lr': .001
}])
deepen_data = {
'samples': [],
'labels': [],
'embedding_indices': []
}
if self.total_iterations > 5000 and np.mean(
self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]) < self.covergence_epsilon:
training_done = True
def evaluate_on_test_data(self, model, load_in_model=False):
"""
Evaluate performance of a trained network tuned upon the alpha divergence loss.
This is tested on 20% of the data and will be stored in a text file.
Note this function is called after training convergence
:return:
"""
# define new optimizer that only optimizes gradient
num_schedules = 75
loss_func = AlphaLoss()
load_directory = '/home/ghost/PycharmProjects/bayesian_prolo/scheduling_env/datasets/test/' + str(
num_schedules) + '_inf_hetero_deadline_pairwise.pkl'
data = pickle.load(open(load_directory, "rb"))
X, Y, schedule_array = create_new_data(num_schedules, data)
start_of_each_set_twenty = create_sets_of_20_from_x_for_pairwise_comparisions(X)
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
if load_in_model:
model.load_state_dict(torch.load('/home/ghost/PycharmProjects/bayesian_prolo/saved_models/pairwise_saved_models/model_homog.tar')['nn_state_dict'])
for j in range(0, num_schedules):
schedule_bounds = schedule_array[j]
step = schedule_bounds[0]
while step < schedule_bounds[1]:
probability_matrix = np.zeros((20, 20))
for m, counter in enumerate(range(step, step + 20)):
phi_i = X[counter]
phi_i_numpy = np.asarray(phi_i)
# for each set of twenty
for n, second_counter in enumerate(range(step, step + 20)):
# fill entire array with diagnols set to zero
if second_counter == counter: # same as m = n
continue
phi_j = X[second_counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
if torch.cuda.is_available():
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)).cuda())
else:
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)))
# push through nets
preference_prob = model.forward(feature_input)
probability_matrix[m][n] = preference_prob[0].data.detach()[0].item()
probability_matrix[n][m] = preference_prob[0].data.detach()[1].item()
# Set of twenty is completed
column_vec = np.sum(probability_matrix, axis=1)
# top 1
choice = np.argmax(column_vec)
# top 3
_, top_three = torch.topk(torch.Tensor(column_vec), 3)
# Then do training update loop
truth = Y[step]
# index top 1
if choice == truth:
prediction_accuracy[0] += 1
# index top 3
if truth in top_three:
prediction_accuracy[1] += 1
# add average loss to array
step += 20
# schedule finished
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20, ' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', j)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
self.save_performance_results(percentage_accuracy_top1, percentage_accuracy_top3, 'inf_DDT'+ str(self.num_schedules))
def train(self):
"""
Trains NN.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
training_done = False
# cv_cutoff = int(.8 * len(self.start_of_each_set_twenty)) # TODO: delete or to not delete
loss_func = AlphaLoss()
# variables to keep track of loss and number of tasks trained over
running_loss_predict_tasks = 0
num_iterations_predict_task = 0
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(
len(self.start_of_each_set_twenty) * .8)
set_of_twenty = self.start_of_each_set_twenty[
rand_timestep_within_sched]
truth = self.Y[set_of_twenty]
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, set_of_twenty)
load_in_embedding_bnn(self.model, self.embedding_list,
which_schedule)
# find feature vector of true action taken
phi_i_num = truth + set_of_twenty
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)).cuda())
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]))
output = self.model.forward(feature_input)
self.opt.zero_grad()
self.embedding_optimizer.zero_grad()
loss = loss_func.forward(P, output, self.alpha)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ',
self.total_iterations)
if self.total_iterations > 7500:
if loss.item() < .001 or loss.item() > 55:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
self.embedding_optimizer.step()
else:
if loss.item() < .001 or loss.item() > 55:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
self.embedding_optimizer.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
# second loop
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)).cuda())
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]))
output = self.model.forward(feature_input)
self.opt.zero_grad()
self.embedding_optimizer.zero_grad()
loss = loss_func.forward(P, output, self.alpha)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ',
self.total_iterations, ' at',
num_iterations_predict_task)
if self.total_iterations > 7500:
if loss.item() < .001 or loss.item() > 55:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
self.embedding_optimizer.step()
else:
if loss.item() < .001 or loss.item() > 55:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
self.embedding_optimizer.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
self.embedding_list = store_embedding_back_bnn(
self.model, self.embedding_list, which_schedule)
self.total_loss_array.append(running_loss_predict_tasks /
num_iterations_predict_task)
num_iterations_predict_task = 0
running_loss_predict_tasks = 0
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
self.save_trained_nets('bnn_small' + str(self.num_schedules))
if self.total_iterations > 5000 and np.mean(
self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]
) < self.convergence_epsilon:
training_done = True
def train(self):
"""
Trains BDT.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
threshold = .1
training_done = False
loss_func = AlphaLoss()
# variables to keep track of loss and number of tasks trained over
running_loss_predict_tasks = 0
num_iterations_predict_task = 0
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(
len(self.start_of_each_set_twenty))
set_of_twenty = self.start_of_each_set_twenty[
rand_timestep_within_sched]
truth = self.Y[set_of_twenty]
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, set_of_twenty)
load_in_embedding(self.model, self.embedding_list, which_schedule)
# find feature vector of true action taken
phi_i_num = truth + set_of_twenty
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
# label = add_noise_pairwise(label, self.noise_percentage)
feature_input, P = transform_into_torch_vars(
feature_input, self.distribution_epsilon, True,
torch.cuda.is_available())
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# NAN check (fix is in the bnn file)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ',
self.total_iterations)
# prepare optimizer, compute gradient, update params
self.opt.zero_grad()
self.embedding_optimizer.zero_grad()
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
self.embedding_optimizer.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
feature_input, P = transform_into_torch_vars(
feature_input, self.distribution_epsilon, False,
torch.cuda.is_available())
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# if num_iterations_predict_task % 5 == 0:
# print('loss is :', loss.item())
# clip any very high gradients
# prepare optimizer, compute gradient, update params
self.opt.zero_grad()
self.embedding_optimizer.zero_grad()
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
self.embedding_optimizer.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
self.embedding_list = store_embedding_back(self.model,
self.embedding_list,
which_schedule)
self.total_loss_array.append(running_loss_predict_tasks /
num_iterations_predict_task)
num_iterations_predict_task = 0
running_loss_predict_tasks = 0
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
self.save_trained_nets('BDDT' + str(self.num_schedules))
self.evaluate_on_test_data()
if self.total_iterations > 5000 and np.mean(
self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]) < self.covergence_epsilon:
training_done = True
def evaluate_on_test_data(self, load_in_model=False):
"""
Evaluate performance of a trained network tuned upon the alpha divergence loss.
Note this function is called after training convergence
:return:
"""
# define new optimizer that only optimizes gradient
num_schedules = 75
loss_func = AlphaLoss()
load_directory = '/home/ghost/PycharmProjects/bayesian_prolo/scheduling_env/datasets/test/' + str(
num_schedules) + '_inf_hetero_deadline_pairwise.pkl'
data = pickle.load(open(load_directory, "rb"))
X, Y, schedule_array = create_new_data(num_schedules, data)
start_of_each_set_twenty = create_sets_of_20_from_x_for_pairwise_comparisions(
X)
embedding_optimizer = torch.optim.RMSprop([{
'params': self.model.bayesian_embedding,
'lr': .001
}])
embedding_list = [torch.ones(8) * 1 / 3 for _ in range(num_schedules)]
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
if load_in_model:
self.model.load_state_dict(
torch.load(
'/home/ghost/PycharmProjects/bayesian_prolo/saved_models/pairwise_saved_models/model_homog.tar'
)['nn_state_dict'])
for j in range(0, num_schedules):
schedule_bounds = schedule_array[j]
step = schedule_bounds[0]
load_in_embedding(self.model, embedding_list, j)
while step < schedule_bounds[1]:
probability_matrix = np.zeros((20, 20))
for m, counter in enumerate(range(step, step + 20)):
phi_i = X[counter]
phi_i_numpy = np.asarray(phi_i)
# for each set of twenty
for n, second_counter in enumerate(range(step, step + 20)):
# fill entire array with diagnols set to zero
if second_counter == counter: # same as m = n
continue
phi_j = X[second_counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
# push through nets
preference_prob = self.model.forward(feature_input)
probability_matrix[m][n] = preference_prob[
0].data.detach()[0].item()
# probability_matrix[n][m] = preference_prob[0].data.detach()[1].item()
# Set of twenty is completed
column_vec = np.sum(probability_matrix, axis=1)
# top 1
choice = np.argmax(column_vec)
# top 3
_, top_three = torch.topk(torch.Tensor(column_vec), 3)
# Then do training update loop
truth = Y[step]
# index top 1
if choice == truth:
prediction_accuracy[0] += 1
# index top 3
if truth in top_three:
prediction_accuracy[1] += 1
# forward
phi_i_num = truth + step # old method: set_of_twenty[0] + truth
phi_i = X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
for counter in range(step, step + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
# label = add_noise_pairwise(label, self.noise_percentage)
feature_input, P = transform_into_torch_vars(
feature_input, self.distribution_epsilon, True,
torch.cuda.is_available())
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# prepare optimizer, compute gradient, update params
if loss.item() < .001 or loss.item() > 50:
pass
else:
embedding_optimizer.zero_grad()
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
embedding_optimizer.step()
for counter in range(step, step + 20):
if counter == phi_i_num:
continue
else:
phi_j = X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
feature_input, P = transform_into_torch_vars(
feature_input, self.distribution_epsilon, False,
torch.cuda.is_available())
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# print('loss is :', loss.item())
# clip any very high gradients
# prepare optimizer, compute gradient, update params
if loss.item() < .001 or loss.item() > 50:
pass
else:
embedding_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
embedding_optimizer.step()
# add average loss to array
embedding_list = store_embedding_back(self.model,
embedding_list, j)
step += 20
# schedule finished
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20,
' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', j)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
print('top1_mean for ', self.alpha, ' is : ',
np.mean(percentage_accuracy_top1))
def evaluate_on_test_data(self):
"""
Evaluate performance of a trained network.
This is tested on 20% of the data and will be stored in a text file.
:return:
"""
loss_func = AlphaLoss()
num_schedules = 75
# load in new data
load_directory = '/home/ghost/PycharmProjects/bayesian_prolo/scheduling_env/datasets/test/' + str(
num_schedules) + '_inf_hetero_deadline_naive.pkl'
data = pickle.load(open(load_directory, "rb"))
X, Y, schedule_array = create_new_dataset(data, num_schedules)
for i, each_element in enumerate(X):
X[i] = each_element + list(range(20))
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
embedding_optimizer = torch.optim.SGD(
self.model.EmbeddingList.parameters(), lr=.001)
embedding_list = [
torch.ones(1, 8) * 1 / 3 for i in range(num_schedules)
]
for i, schedule in enumerate(schedule_array):
load_in_embedding_bnn(self.model, embedding_list, i)
for count in range(schedule[0], schedule[1] + 1):
net_input = X[count]
truth = Y[count]
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(
1, 242)).cuda())
truth = Variable(
torch.Tensor(
np.asarray(truth).reshape(1)).cuda().long())
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth] = 1 - 19 * self.distribution_epsilon
else:
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(1, 242)))
truth = Variable(torch.Tensor(
np.asarray(truth).reshape(1)))
P = Variable(torch.Tensor(np.ones((1, 20))))
P *= self.distribution_epsilon
P[0][truth] = 1 - 19 * self.distribution_epsilon
#####forward#####
output = self.model.forward(input_nn)
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .05 or loss.item() > 5:
pass
else:
embedding_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
embedding_optimizer.step()
index = torch.argmax(output).item()
# top 3
_, top_three = torch.topk(output, 3)
if index == truth.item():
prediction_accuracy[0] += 1
if truth.item() in top_three.detach().cpu().tolist()[0]:
prediction_accuracy[1] += 1
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20,
' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', i)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
store_embedding_back_bnn(self.model, embedding_list, i)
print(np.mean(percentage_accuracy_top1))
self.save_performance_results(
percentage_accuracy_top1, percentage_accuracy_top3,
'inf_bnn_small_' + str(self.num_schedules))
def train(self):
"""
Trains NN.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
total_iterations = 0
convergence_epsilon = .01
when_to_save = 1000
distribution_epsilon = .0001
training_done = False
total_loss_array = []
loss_func = AlphaLoss(.9)
# variables to keep track of loss and number of tasks trained over
while not training_done:
# sample a timestep before the cutoff for cross_validation
set_of_twenty = np.random.choice(self.start_of_each_set_twenty)
which_schedule = find_which_schedule_this_belongs_to(self.schedule_array, set_of_twenty)
# get actual task scheduled
truth = self.Y[set_of_twenty]
# choose cluster based on value produced by gmm
augment_proba = self.label[which_schedule]
# find feature vector of true action taken
phi_i_num = truth + set_of_twenty
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
running_loss_predict_tasks = 0
num_iterations_predict_task = 0
# iterate over pairwise comparisons
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
if torch.cuda.is_available():
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)).cuda())
feature_input = torch.cat(feature_input, torch.Tensor(augment_proba))
P = Variable(torch.Tensor([1 - distribution_epsilon, distribution_epsilon]).cuda())
else:
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(torch.Tensor([1 - distribution_epsilon, distribution_epsilon]))
output = self.model.forward(feature_input)
if torch.isnan(output[0][0]).item() == 1:
print('hi')
self.opt.zero_grad()
loss = loss_func.forward(P, output)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ', total_iterations, ' at', num_iterations_predict_task)
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
# second loop
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)).cuda())
P = Variable(torch.Tensor([distribution_epsilon, 1 - distribution_epsilon]).cuda())
else:
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(torch.Tensor([distribution_epsilon, 1 - distribution_epsilon]))
output = self.model.forward(feature_input)
if torch.isnan(output[0][0]).item() == 1:
print('hi')
self.opt.zero_grad()
loss = loss_func.forward(P, output)
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
total_loss_array.append(running_loss_predict_tasks / num_iterations_predict_task)
total_iterations += 1
if total_iterations % 50 == 49:
print('total loss (average for each 40, averaged) at iteration ', total_iterations, ' is ', np.mean(total_loss_array[-40:]))
if total_iterations % when_to_save == when_to_save - 1:
self.save_trained_nets('gmm_nn_small' + str(self.num_schedules))
if total_iterations > 5000 and np.mean(total_loss_array[-100:]) - np.mean(total_loss_array[-500:]) < convergence_epsilon:
training_done = True
def train(self):
"""
Trains BDT.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
# loss = nn.CrossEntropyLoss()
training_done = False
cv_cutoff = int(.8 * len(self.start_of_each_set_twenty))
loss_func = AlphaLoss()
# variables to keep track of loss and number of tasks trained over
running_loss_predict_tasks = 0
num_iterations_predict_task = 0
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(cv_cutoff)
set_of_twenty = self.start_of_each_set_twenty[rand_timestep_within_sched]
truth = self.Y[set_of_twenty]
which_schedule = self.find_which_schedule_this_belongs_to(set_of_twenty)
load_in_embedding(self.model, self.embedding_list, which_schedule)
# find feature vector of true action taken
phi_i_num = truth + set_of_twenty
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
# label = add_noise_pairwise(label, self.noise_percentage)
if torch.cuda.is_available():
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 12)).cuda())
P = Variable(torch.Tensor([1 - self.distribution_epsilon, self.distribution_epsilon]).cuda())
else:
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 12)))
P = Variable(torch.Tensor([1 - self.distribution_epsilon, self.distribution_epsilon]))
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ', self.total_iterations)
return
# TODO: can prob just set training to true here if network still outputs proper stuff
# prepare optimizer, compute gradient, update params
self.opt.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
print('after ', num_iterations_predict_task, ' the embedding has changed to :', self.model.state_dict()['bayesian_embedding'])
num_iterations_predict_task += 1
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 12)).cuda())
P = Variable(torch.Tensor([self.distribution_epsilon, 1 - self.distribution_epsilon]).cuda())
else:
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 12)))
P = Variable(torch.Tensor([self.distribution_epsilon, 1 - self.distribution_epsilon]))
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# if num_iterations_predict_task % 5 == 0:
# print('loss is :', loss.item())
# clip any very high gradients
# prepare optimizer, compute gradient, update params
self.opt.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)
self.opt.step()
print('after ', num_iterations_predict_task, ' the embedding has changed to :', self.model.state_dict()['bayesian_embedding'])
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
# add average loss to array
# print(list(self.model.parameters()))
store_embedding_back(self.model, self.embedding_list, which_schedule)
self.total_loss_array.append(running_loss_predict_tasks / num_iterations_predict_task)
num_iterations_predict_task = 0
running_loss_predict_tasks = 0
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)', np.mean(self.total_loss_array[-40:]))
# TODO: change running loss to actual loss
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
# self.plot_nn()
print(self.embedding_list)
self.save_trained_nets()
# self.evaluate()
if self.total_iterations > 10000 and np.mean(self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]) < self.covergence_epsilon:
training_done = True
def evaluate_on_test_data(self, load_in_model=False):
"""
Evaluate performance of a trained network tuned upon the alpha divergence loss.
Note this function is called after training convergence
:return:
"""
num_schedules = 75
loss_func = AlphaLoss()
# load in new data
load_directory = '/home/ghost/PycharmProjects/bayesian_prolo/scheduling_env/datasets/test/' + str(
num_schedules) + '_inf_hetero_deadline_naive.pkl'
data = pickle.load(open(load_directory, "rb"))
X, Y, schedule_array = create_new_dataset(num_schedules=num_schedules,
data=data)
for i, each_element in enumerate(X):
X[i] = each_element + list(range(20))
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
embedding_optimizer = torch.optim.Adam(
self.model.EmbeddingList.parameters(), lr=.001)
embedding_list = [
torch.ones(1, 8) * 1 / 3 for i in range(num_schedules)
]
if load_in_model: # TODO: somehow get the string when the update_model flag is true
self.model.load_state_dict(
torch.load(
'/home/ghost/PycharmProjects/bayesian_prolo/saved_models/pairwise_saved_models/NN_homog.tar'
)['nn_state_dict'])
for i, schedule in enumerate(schedule_array):
self.model.reinitialize_hidden_to_random()
load_in_embedding_bnn(self.model, embedding_list, i)
for count in range(schedule[0], schedule[1] + 1):
previous_hidden_state = tuple(
[t.detach().cuda() for t in self.model.hidden])
net_input = X[count]
truth = Y[count]
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(
1, 242)).cuda())
truth = Variable(
torch.Tensor(
np.asarray(truth).reshape(1)).cuda().long())
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth] = 1 - 19 * self.distribution_epsilon
else:
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(1, 242)))
truth = Variable(torch.Tensor(
np.asarray(truth).reshape(1)))
P = Variable(torch.Tensor(np.ones((1, 20))))
P *= self.distribution_epsilon
P[0][truth] = 1 - 19 * self.distribution_epsilon
#####forward#####
output = self.model.forward(input_nn, previous_hidden_state)
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .05 or loss.item() > 5:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
embedding_optimizer.step()
index = torch.argmax(output).item()
# top 3
_, top_three = torch.topk(output, 3)
if index == truth.item():
prediction_accuracy[0] += 1
if truth.item() in top_three.detach().cpu().tolist()[0]:
prediction_accuracy[1] += 1
store_embedding_back_bnn(self.model, embedding_list, i)
# schedule finished
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20,
' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', i)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
self.save_performance_results(
percentage_accuracy_top1, percentage_accuracy_top3,
'inf_blstm_small_' + str(self.num_schedules))
def train(self):
"""
Trains BDT.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
threshold = .1
training_done = False
loss_func = AlphaLoss()
# deepening data
deepen_data = {'samples': [], 'labels': [], 'embedding_indices': []}
# variables to keep track of loss and number of tasks trained over
running_loss_predict_tasks = 0
num_iterations_predict_task = 0
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(
len(self.start_of_each_set_twenty))
set_of_twenty = self.start_of_each_set_twenty[
rand_timestep_within_sched]
truth = self.Y[set_of_twenty]
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, set_of_twenty)
load_in_embedding(self.model, self.embedding_list, which_schedule)
# find feature vector of true action taken
phi_i_num = truth + set_of_twenty
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
deepen_data['samples'].append(np.array(feature_input))
# label = add_noise_pairwise(label, self.noise_percentage)
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)).cuda())
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]))
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# NAN check (fix is in the bnn file)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ',
self.total_iterations)
# prepare optimizer, compute gradient, update params
self.opt.zero_grad()
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
deepen_data['labels'].extend([0])
deepen_data['embedding_indices'].extend([which_schedule])
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
deepen_data['samples'].append(np.array(feature_input))
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)).cuda())
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]))
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# if num_iterations_predict_task % 5 == 0:
# print('loss is :', loss.item())
# clip any very high gradients
# prepare optimizer, compute gradient, update params
self.opt.zero_grad()
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
deepen_data['labels'].extend([1])
deepen_data['embedding_indices'].extend([which_schedule])
# add average loss to array
# print(list(self.model.parameters()))
self.embedding_list = store_embedding_back(self.model,
self.embedding_list,
which_schedule)
self.total_loss_array.append(running_loss_predict_tasks /
num_iterations_predict_task)
num_iterations_predict_task = 0
running_loss_predict_tasks = 0
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
self.save_trained_nets('BDDT' + str(self.num_schedules))
threshold -= .025
# if self.total_iterations % 500 == 499:
# self.model = deepen_with_embeddings(self.model, deepen_data, self.embedding_list, max_depth=self.max_depth, threshold=threshold)
# params = list(self.model.parameters())
# del params[0]
# self.opt = torch.optim.RMSprop([{'params': params}, {'params': self.model.bayesian_embedding, 'lr': .001}])
# deepen_data = {
# 'samples': [],
# 'labels': [],
# 'embedding_indices': []
# }
if self.total_iterations > 5000 and np.mean(
self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]) < self.covergence_epsilon:
training_done = True
def train(self):
"""
Trains NN.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
sets_of_twenty = None
training_done = False
loss_func = AlphaLoss()
# variables to keep track of loss and number of tasks trained over
running_loss_predict_tasks = 0
num_iterations_predict_task = 0
while not training_done:
# sample a timestep before the cutoff for cross_validation
# Quick Fix
found_a_suitable_candidate = False
while not found_a_suitable_candidate:
rand_timestep_within_sched = np.random.randint(
len(self.start_of_each_set_twenty))
set_of_twenty = self.start_of_each_set_twenty[
rand_timestep_within_sched]
which_schedule = find_which_schedule_this_belongs_to(
self.schedule_array, set_of_twenty)
if set_of_twenty + 59 > self.schedule_array[which_schedule][1]:
pass
else:
found_a_suitable_candidate = True
sets_of_twenty = [
set_of_twenty, set_of_twenty + 20, set_of_twenty + 40
]
self.model.reinitialize_hidden_to_random()
for set_of_twenty in sets_of_twenty:
truth = self.Y[set_of_twenty]
# find feature vector of true action taken
phi_i_num = truth + set_of_twenty
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
previous_hidden_state = tuple(
[t.detach().cuda() for t in self.model.hidden])
# iterate over pairwise comparisons
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]))
output = self.model.forward(feature_input,
previous_hidden_state)
if torch.isnan(output[0][0]).item() == 1:
print('hi')
self.opt.zero_grad()
loss = loss_func.forward(P, output, self.alpha)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ',
self.total_iterations, ' at',
num_iterations_predict_task)
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
# second loop
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]))
output = self.model.forward(feature_input,
previous_hidden_state)
if torch.isnan(output[0][0]).item() == 1:
print('hi')
self.opt.zero_grad()
loss = loss_func.forward(P, output, self.alpha)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ',
self.total_iterations, ' at',
num_iterations_predict_task)
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
self.total_loss_array.append(running_loss_predict_tasks /
num_iterations_predict_task)
num_iterations_predict_task = 0
running_loss_predict_tasks = 0
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)',
np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
self.save_trained_nets('lstm_small' + str(self.num_schedules))
if self.total_iterations > 1500 and np.mean(
self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]
) < self.convergence_epsilon:
training_done = True
def evaluate_on_test_data(self, load_in_model=False):
"""
Evaluate performance of a trained network tuned upon the alpha divergence loss.
Note this function is called after training convergence
:return:
"""
num_schedules = 75
# load in new data
loss_func = AlphaLoss()
load_directory = '/home/ghost/PycharmProjects/bayesian_prolo/scheduling_env/datasets/test/' + str(
num_schedules) + '_inf_hetero_deadline_pairwise.pkl'
data = pickle.load(open(load_directory, "rb"))
X, Y, schedule_array = create_new_data(num_schedules, data)
start_of_each_set_twenty = create_sets_of_20_from_x_for_pairwise_comparisions(
X)
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
embedding_optimizer = torch.optim.Adam(
self.model.EmbeddingList.parameters(), lr=.001)
embedding_list = [
torch.ones(1, 8) * 1 / 3 for i in range(num_schedules)
]
if load_in_model: # TODO: somehow get the string when the update_model flag is true
self.model.load_state_dict(
torch.load(
'/home/ghost/PycharmProjects/bayesian_prolo/saved_models/pairwise_saved_models/NN_homog.tar'
)['nn_state_dict'])
for j in range(0, num_schedules):
schedule_bounds = schedule_array[j]
step = schedule_bounds[0]
load_in_embedding_bnn(self.model, embedding_list, j)
self.model.reinitialize_hidden_to_random()
while step < schedule_bounds[1]:
probability_matrix = np.zeros((20, 20))
previous_hidden_state = tuple(
[t.detach().cuda() for t in self.model.hidden])
for m, counter in enumerate(range(step, step + 20)):
phi_i = X[counter]
phi_i_numpy = np.asarray(phi_i)
# for each set of twenty
for n, second_counter in enumerate(range(step, step + 20)):
# fill entire array with diagonals set to zero
if second_counter == counter: # same as m = n
continue
phi_j = X[second_counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
# push through nets
preference_prob = self.model.forward(
feature_input, previous_hidden_state)
probability_matrix[m][n] = preference_prob[
0].data.detach()[0].item(
) # TODO: you can do a check if only this line leads to the same thing as the line below
# probability_matrix[n][m] = preference_prob[0].data.detach()[1].item()
# Set of twenty is completed
column_vec = np.sum(probability_matrix, axis=1)
# top 1
choice = np.argmax(column_vec)
# top 3
_, top_three = torch.topk(torch.Tensor(column_vec), 3)
# Then do training update loop
truth = Y[step]
# index top 1
if choice == truth:
prediction_accuracy[0] += 1
# index top 3
if truth in top_three:
prediction_accuracy[1] += 1
# forward
phi_i_num = truth + step # old method: set_of_twenty[0] + truth
phi_i = X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
for counter in range(step, step + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
# label = add_noise_pairwise(label, self.noise_percentage)
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
1 - self.distribution_epsilon,
self.distribution_epsilon
]))
output = self.model(feature_input,
previous_hidden_state)
loss = loss_func.forward(P, output, self.alpha)
# prepare optimizer, compute gradient, update params
if loss.item() < .001 or loss.item() > 50:
pass
else:
embedding_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
embedding_optimizer.step()
for counter in range(step, step + 20):
if counter == phi_i_num:
continue
else:
phi_j = X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(
torch.Tensor(feature_input.reshape(1,
13)).cuda())
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]).cuda())
else:
feature_input = Variable(
torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(
torch.Tensor([
self.distribution_epsilon,
1 - self.distribution_epsilon
]))
output = self.model(feature_input,
previous_hidden_state)
loss = loss_func.forward(P, output, self.alpha)
# print('loss is :', loss.item())
# clip any very high gradients
# prepare optimizer, compute gradient, update params
if loss.item() < .001 or loss.item() > 50:
pass
else:
embedding_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), 0.5)
embedding_optimizer.step()
# add average loss to array
store_embedding_back_bnn(self.model, embedding_list, j)
step += 20
# schedule finished
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20,
' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', j)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
self.save_performance_results(
percentage_accuracy_top1, percentage_accuracy_top3,
'inf_blstm_small_' + str(self.num_schedules))
def train(self):
"""
Trains BDT.
Randomly samples a schedule and timestep within that schedule, produces training data using x_i - x_j
and trains upon that.
:return:
"""
# loss = nn.CrossEntropyLoss()
training_done = False
loss_func = AlphaLoss()
# variables to keep track of loss and number of tasks trained over
running_loss_predict_tasks = 0
num_iterations_predict_task = 0
while not training_done:
# sample a timestep before the cutoff for cross_validation
rand_timestep_within_sched = np.random.randint(len(self.start_of_each_set_twenty))
set_of_twenty = self.start_of_each_set_twenty[rand_timestep_within_sched]
truth = self.Y[set_of_twenty]
# find feature vector of true action taken
phi_i_num = truth + set_of_twenty
phi_i = self.X[phi_i_num]
phi_i_numpy = np.asarray(phi_i)
# iterate over pairwise comparisons
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num: # if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_i_numpy - phi_j_numpy
# label = add_noise_pairwise(label, self.noise_percentage)
if torch.cuda.is_available():
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)).cuda())
P = Variable(torch.Tensor([1 - self.distribution_epsilon, self.distribution_epsilon]).cuda())
else:
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(torch.Tensor([1 - self.distribution_epsilon, self.distribution_epsilon]))
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
if torch.isnan(loss):
print(self.alpha, ' :nan occurred at iteration ', self.total_iterations)
# prepare optimizer, compute gradient, update params
self.opt.zero_grad()
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
for counter in range(set_of_twenty, set_of_twenty + 20):
if counter == phi_i_num:
continue
else:
phi_j = self.X[counter]
phi_j_numpy = np.asarray(phi_j)
feature_input = phi_j_numpy - phi_i_numpy
if torch.cuda.is_available():
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)).cuda())
P = Variable(torch.Tensor([self.distribution_epsilon, 1 - self.distribution_epsilon]).cuda())
else:
feature_input = Variable(torch.Tensor(feature_input.reshape(1, 13)))
P = Variable(torch.Tensor([self.distribution_epsilon, 1 - self.distribution_epsilon]))
output = self.model(feature_input)
loss = loss_func.forward(P, output, self.alpha)
# prepare optimizer, compute gradient, update params
self.opt.zero_grad()
if loss.item() < .001 or loss.item() > 50:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)
self.opt.step()
running_loss_predict_tasks += loss.item()
num_iterations_predict_task += 1
# add average loss to array
# print(list(self.model.parameters()))
self.total_loss_array.append(running_loss_predict_tasks / num_iterations_predict_task)
num_iterations_predict_task = 0
running_loss_predict_tasks = 0
self.total_iterations += 1
if self.total_iterations > 25 and self.total_iterations % 50 == 1:
print('total iterations is', self.total_iterations)
print('total loss (average for each 40, averaged)', np.mean(self.total_loss_array[-40:]))
if self.total_iterations > 0 and self.total_iterations % self.when_to_save == self.when_to_save - 1:
# self.plot_nn()
self.save_trained_nets('DDT' + str(self.num_schedules))
# self.evaluate()
if self.total_iterations > 2500 and np.mean(self.total_loss_array[-100:]) - np.mean(
self.total_loss_array[-500:]) < self.covergence_epsilon:
training_done = True
def evaluate_on_test_data(self, load_in_model=False):
"""
Evaluate performance of a trained network tuned upon the alpha divergence loss.
Note this function is called after training convergence
:return:
"""
# define new optimizer that only optimizes gradient
num_schedules = 75
loss_func = AlphaLoss()
load_directory = '/home/ghost/PycharmProjects/bayesian_prolo/scheduling_env/datasets/test/' + str(
num_schedules) + '_inf_hetero_deadline_naive.pkl'
data = pickle.load(open(load_directory, "rb"))
X, Y, schedule_array = create_new_dataset(num_schedules=num_schedules,
data=data)
for i, each_element in enumerate(X):
X[i] = each_element + list(range(20))
embedding_optimizer = torch.optim.RMSprop([{
'params': self.model.bayesian_embedding,
'lr': .001
}])
embedding_list = [torch.ones(8) * 1 / 3 for _ in range(num_schedules)]
prediction_accuracy = [0, 0]
percentage_accuracy_top1 = []
percentage_accuracy_top3 = []
if load_in_model:
self.model.load_state_dict(
torch.load(
'/home/ghost/PycharmProjects/bayesian_prolo/saved_models/pairwise_saved_models/model_homog.tar'
)['nn_state_dict'])
for i, schedule in enumerate(schedule_array):
load_in_embedding(self.model, embedding_list, i)
for count in range(schedule[0], schedule[1] + 1):
net_input = X[count]
truth = Y[count]
if torch.cuda.is_available():
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(
1,
242)).cuda()) # change to 5 to increase batch size
P = Variable(torch.Tensor(np.ones((1, 20)))).cuda()
P *= self.distribution_epsilon
P[0][truth] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(
np.asarray(truth).reshape(1)).cuda().long())
else:
input_nn = Variable(
torch.Tensor(np.asarray(net_input).reshape(1, 242)))
P = Variable(
torch.Tensor(
np.ones((1, 20) * self.distribution_epsilon)))
P[0][truth] = 1 - 19 * self.distribution_epsilon
truth = Variable(
torch.Tensor(np.asarray(truth).reshape(1)).long())
#####forward#####
output = self.model.forward(input_nn)
embedding_optimizer.zero_grad()
loss = loss_func.forward(P, output, self.alpha)
if loss.item() < .001 or loss.item() > 30:
pass
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
0.5)
embedding_optimizer.step()
index = torch.argmax(output).item()
# top 3
_, top_three = torch.topk(output, 3)
if index == truth.item():
prediction_accuracy[0] += 1
if truth.item() in top_three.detach().cpu().tolist()[0]:
prediction_accuracy[1] += 1
# add average loss to array
embedding_list = store_embedding_back(self.model, embedding_list,
i)
# schedule finished
print('Prediction Accuracy: top1: ', prediction_accuracy[0] / 20,
' top3: ', prediction_accuracy[1] / 20)
print('schedule num:', i)
percentage_accuracy_top1.append(prediction_accuracy[0] / 20)
percentage_accuracy_top3.append(prediction_accuracy[1] / 20)
prediction_accuracy = [0, 0]
self.save_performance_results(percentage_accuracy_top1,
percentage_accuracy_top3,
'inf_BDT' + str(self.num_schedules))