def main(unused_argv):
batchsize_video = 1
dir_path = os.path.dirname(os.path.realpath(__file__))
train_folder = os.path.join(dir_path, "train/")
test_folder = os.path.join(dir_path, "test/")
train_target = os.path.join(dir_path, 'train_target.csv')
my_solution_file = os.path.join(dir_path, 'solution.csv')
tf_record_dir = os.path.join(dir_path, 'tf_records')
os.makedirs(tf_record_dir, exist_ok=True)
tf_record_train = os.path.join(tf_record_dir, 'train' + '.tfrecords')
tf_record_test = os.path.join(tf_record_dir, 'test' + '.tfrecords')
if not os.path.exists(tf_record_train):
x_train = get_videos_from_folder(train_folder)
y_train = get_target_from_csv(train_target)
save_tf_record(x_train, tf_record_train, y=y_train)
if not os.path.exists(tf_record_test):
x_test = get_videos_from_folder(test_folder)
save_tf_record(x_test, tf_record_test)
# Create the Estimator
classifier = tf.estimator.Estimator(model_fn=cnn_model_fn,
model_dir="\\tmp\\model")
# Set up logging for predictions
# Log the values in the "Softmax" tensor with label "probabilities"
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log,
every_n_iter=50)
print('{}: Train'.format(datetime.now().strftime("%H:%M:%S")))
# Train the model
classifier.train(input_fn=lambda: input_fn_from_dataset(
tf_record_train, batch_size=batchsize_video),
max_steps=1,
hooks=[logging_hook])
print('{}: Evaluate'.format(datetime.now().strftime("%H:%M:%S")))
# Evaluate the model and print results
#eval_input_fn = tf.estimator.inputs.numpy_input_fn(x={"x": eval_data}, y=eval_labels, num_epochs=1, shuffle=False)
eval_results = classifier.evaluate(input_fn=lambda: input_fn_from_dataset(
tf_record_test, batch_size=batchsize_video))
print(eval_results)
print('{}: Predict'.format(datetime.now().strftime("%H:%M:%S")))
pred = classifier.predict(
input_fn=lambda: input_fn_from_dataset(tf_record_test,
batch_size=batchsize_video,
num_epochs=1,
shuffle=False))
print('{}: Save solution to {}'.format(datetime.now().strftime("%H:%M:%S"),
my_solution_file))
solution = prob_positive_class_from_prediction(pred)
save_solution(my_solution_file, solution)
def compute_success_rate():
files = [
os.path.join('.', 'Maps', f) for f in os.listdir('Maps')
if f[-3:] == 'mat' and f[-12:-4] != 'solution'
]
print('Files Found: {}'.format(files))
success_count = {}
for window_ratio in [1, 2, 3, 4, 5]:
success_count[window_ratio] = 10
for f in files:
cost_matrix = load_cost_matrix(f)
num_nodes = cost_matrix.shape[0]
score_vector = np.ones(num_nodes)
task_windows = setup_task_windows(score_vector, window_ratio)
max_cost = get_starting_cost(cost_matrix, task_windows)
try:
plan, visit_times, profit, cost, solver_stats = get_solution(
score_vector, cost_matrix, task_windows, max_cost)
except ValueError:
print('Failed with cost {}'.format(max_cost))
success_count[window_ratio] -= 1
continue
wait_times = compute_wait_times(plan, cost_matrix, visit_times)
visit_order_waits, visit_times_waits = get_arrive_depart_pairs(
plan, visit_times, wait_times, cost)
save_solution(f,
visit_order_waits,
visit_times_waits,
solver_type='tw')
g, tour, verified_cost = build_graph(plan, score_vector,
cost_matrix)
print(f)
msg = 'The maximum profit tour found is \n'
for idx, k in enumerate(tour):
msg += str(k)
if idx < len(tour) - 1:
msg += ' -> '
else:
msg += ' -> 0'
print(msg)
msg = 'Profit: {:.2f}, cost: {:.2f}, verification cost: {:.2f}'
print(msg.format(profit, cost, verified_cost))
print(solver_stats.solve_time)
print(solver_stats.setup_time)
msg = 'Time taken: {} seconds'
time = solver_stats.solve_time + solver_stats.setup_time
print(msg.format(time))
success_count[window_ratio] /= 10.0
print('Success probabilities across constraint ratios')
print(success_count)
return success_count
def compute_solve_times(files):
problem_sizes = [3, 4, 5, 6, 7, 8, 9, 10]
small_map_solve_times = {p: [] for p in problem_sizes}
large_map_solve_times = {p: [] for p in problem_sizes}
for n in problem_sizes:
for f in files:
cost_matrix = load_cost_matrix(f)
# cost_matrix = cost_matrix[0:n, 0:n]
# print('----- Edge Costs -----')
# print(cost_matrix)
num_nodes = cost_matrix.shape[0]
score_vector = np.ones(num_nodes)
task_windows = setup_task_windows(score_vector)
max_cost = get_starting_cost(cost_matrix, task_windows)
try:
plan, visit_times, profit, cost, solver_stats = get_solution(
score_vector, cost_matrix, task_windows, max_cost)
except ValueError:
print('Failed with cost {}'.format(max_cost))
continue
wait_times = compute_wait_times(plan, cost_matrix, visit_times)
visit_order_waits, visit_times_waits = get_arrive_depart_pairs(
plan, visit_times, wait_times, cost)
save_solution(f,
visit_order_waits,
visit_times_waits,
solver_type='tw')
g, tour, verified_cost = build_graph(plan, score_vector,
cost_matrix)
print(f)
msg = 'The maximum profit tour found is \n'
for idx, k in enumerate(tour):
msg += str(k)
if idx < len(tour) - 1:
msg += ' -> '
else:
msg += ' -> 0'
print(msg)
msg = 'Profit: {:.2f}, cost: {:.2f}, verification cost: {:.2f}'
print(msg.format(profit, cost, verified_cost))
msg = 'Time taken: {} seconds'
time = solver_stats.solve_time + solver_stats.setup_time
print(msg.format(time))
# display_results(g, plan, task_windows, visit_times, wait_times, cost)
if f[-12:-7] == '20x20':
small_map_solve_times[n].append(time)
elif f[-12:-7] == '50x50':
large_map_solve_times[n].append(time)
return (small_map_solve_times, large_map_solve_times)
from get_data import get_videos_from_folder, get_target_from_csv import os import numpy as np from utils import save_solution dir_path = os.path.dirname(os.path.realpath(__file__)) train_folder = os.path.join(dir_path, "train/") test_folder = os.path.join(dir_path, "test/") train_target = os.path.join(dir_path, 'train_target.csv') my_solution_file = os.path.join(dir_path, 'solution.csv') x_train = get_videos_from_folder(train_folder) y_train = get_target_from_csv(train_target) x_test = get_videos_from_folder(test_folder) dummy_solution = 0.1 * np.ones(len(x_test)) save_solution(my_solution_file, dummy_solution)
else:
solution[a1 //
2][a1 %
2], solution[a2 //
2][a2 %
2] = solution[a2 //
2][a2 %
2], solution[a1 //
2][a1 %
2]
no_progress_round += 1
return solution
if __name__ == "__main__":
n, m, _, preferences = utils.get_preferences("data/sample_2.txt")
assert n % 2 == 0
solution = solve_2_roommates_greedy(n, m, preferences)
score = utils.compute_score(preferences, solution)
print(f"Found a solution with a greedy algorithm of score {score}\n")
utils.save_solution("output/solution_" + str(score) + ".txt", solution)
solution = solve_2_roommates_conv(n, m, preferences)
score = utils.compute_score(preferences, solution)
print(f"Found a solution with a score of {score}")
utils.save_solution("output/solution_" + str(score) + ".txt", solution)
def get_time_data():
np.random.seed(1)
files = [
os.path.join('.', 'Maps', f) for f in os.listdir('Maps')
if f[-3:] == 'mat' and f[-12:-4] != 'solution'
]
maps20x20 = [f for f in files if '20x20' in f]
maps50x50 = [f for f in files if '20x20' in f]
big_maps = [f for f in files if '100_POI' in f]
runtimes = {}
for n in [4, 6, 8, 10, 12, 14]:
print(n)
runtimes[n] = []
for f in big_maps:
cost_matrix = load_cost_matrix(f)
# high diagonal costs cause numerical errors
# use constraints to prevent travel to self
# diagonal cost must be > 0 for this to work
# but should be low
np.fill_diagonal(cost_matrix, 1)
cost_matrix = cost_matrix[0:n, 0:n]
# print('----- Edge Costs -----')
# print(cost_matrix)
num_nodes = cost_matrix.shape[0]
score_vector = np.ones(num_nodes)
task_windows = setup_task_windows(score_vector)
# print('----- Task Windows -----')
# print(np.around(task_windows, 2).astype('float'))
max_cost = get_starting_cost(cost_matrix, task_windows)
plan, visit_times, profit, cost, solve_time = get_solution(
score_vector, cost_matrix, task_windows, max_cost)
runtimes[n].append(solve_time)
wait_times = compute_wait_times(plan, cost_matrix, visit_times)
visit_order_waits, visit_times_waits = get_arrive_depart_pairs(
plan, visit_times, wait_times, cost)
save_solution(f,
visit_order_waits,
visit_times_waits,
solver_type='tw')
print('----- Visited -----')
print(np.sum(plan, axis=1))
print('----- Plan -----')
print(np.around(plan, 2).astype('int32'))
# print('----- Edge Costs -----')
# print(cost_matrix)
print('----- Wait Times -----')
print(np.around(wait_times, 2))
g, tour, verified_cost = build_graph(plan, score_vector,
cost_matrix)
print(f)
msg = 'The maximum profit tour found is \n'
for idx, k in enumerate(tour):
msg += str(k)
if idx < len(tour) - 1:
msg += ' -> '
else:
msg += ' -> 0'
print(msg)
msg = 'Profit: {:.2f}, cost: {:.2f}, verification cost: {:.2f}'
print(msg.format(profit, cost, verified_cost))
msg = 'Time taken: {} seconds'
print(msg.format(solve_time))
# display_results(g, plan, task_windows, visit_times, wait_times, cost)
with open('results_tw.txt', 'w') as f:
f.write(str(runtimes))
score_vector = np.ones(num_nodes)
task_windows = setup_task_windows(score_vector)
try:
plan, visit_times, profit, cost, solve_time = get_solution(
score_vector, cost_matrix, task_windows, budget)
except ValueError:
print('No solution for {}'.format(f))
continue
wait_times = compute_wait_times(plan, cost_matrix, visit_times)
visit_order_waits, visit_times_waits = get_arrive_depart_pairs(
plan, visit_times, wait_times, cost)
save_solution(f,
visit_order_waits,
visit_times_waits,
solver_type='tw')
# print('----- Visited -----')
# print(np.sum(plan, axis=1))
# print('----- Plan -----')
# print(np.around(plan, 2).astype('int32'))
# print('----- Edge Costs -----')
# print(cost_matrix)
# print('----- Wait Times -----')
# print(np.around(wait_times, 2))
g, tour, verified_cost = build_graph(plan, score_vector,
cost_matrix)
print(f)
model.add(Dense(2, activation='softmax'))
#model.summary()
#compile model using accuracy to measure model performance
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
#train the model
model.load_weights('conv.model')
'''
for i in range(0, len(x_test)):
for j in range(0, len(x_test[i])):
image = x_test[i][j]
result = model.precit(image)
print(image.shape)
'''
answers = []
for i in range(0, len(x_test)):
class_1_prob_sum = 0
print(i / len(x_test))
for j in range(0, len(x_test[i])):
image = x_test[i][j]
image = np.expand_dims(image, 2)
image = np.expand_dims(image, 0)
class_1_prob_sum += model.predict(image)[0][1]
class_1_prob_sum = class_1_prob_sum / len(x_test[i])
answers.append(class_1_prob_sum)
save_solution(my_solution_file, answers)
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='lower right')
plt.savefig("accuracy.png")
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.savefig("loss.png")
plt.show()
y_predict_train = model.predict(x=X_train_reshape,
batch_size=batch_size,
verbose=0,
steps=None)
roc_auc_train = roc_auc_score(y_train, y_predict_train[:, 1])
# make prediction
y_predict_video = model.predict(x=X_test_reshape,
batch_size=batch_size,
verbose=0,
steps=None)
y_predict = y_predict_video[:, 1]
### own code ###
save_solution(my_solution_file, y_predict)
