def benchmark_cost_function(data_sizes):
polygon = pickle.load(open('utils/good_path_for_rectangle.pickle', 'rb'))
save_folder = generate_folder_name()
iterations = 10
results = np.empty((len(data_sizes), iterations, 2), dtype=float)
for i, dsize in enumerate(data_sizes):
data = dg.generate_rectangle_set(dsize)
print("\nRun {} with value {}".format(i+1, dsize))
# Compile functions and warm up GPU
acoc.cost_function_gpu(data.T, polygon)
for j in range(iterations):
utils.print_on_current_line('Iteration {}/{}'.format(j, iterations))
start_cpu = time.clock()
acoc.cost_function(data.T, polygon)
end_cpu = time.clock()
results[i][j][0] = end_cpu - start_cpu
start_gpu = time.clock()
acoc.cost_function_gpu(data.T, polygon)
end_gpu = time.clock()
results[i][j][2] = end_gpu - start_gpu
mean_results = np.mean(results, axis=1).T
acoc_plotter.plot_bar_chart_gpu_benchmark(mean_results, data_sizes, ['CPython', 'GPU'], save_folder, 'results')
np.set_printoptions(precision=7, suppress=False)
print("\nResults: \n{}".format(mean_results))
utils.save_object(mean_results, save_folder, 'results')
def save_plot(fig=None, parent_folder='', file_name=None, extension='png'):
if parent_folder is not None:
directory = os.path.join(SAVE_DIR, parent_folder)
else:
directory = os.path.join(SAVE_DIR, generate_folder_name())
if not os.path.exists(directory):
os.makedirs(directory)
file_name = datetime.utcnow().strftime('%Y-%m-%d %H_%M_%S_%f')[:-5] if file_name is None else file_name
if fig is None:
fig = plt
fig.savefig(os.path.join(directory, file_name + '.' + extension), transparent=False)
class ProductImageInlineForm(InlineFormAdmin):
form_columns = ['id', 'name', 'is_main_image']
column_labels = dict(name="Image")
relative_file_path = get_relative_file_path('products', generate_folder_name())
form_extra_fields = {'name': form.ImageUploadField('Picture', allowed_extensions=['jpg', 'jpeg', 'png'],
base_path=get_file_path(), relative_path=relative_file_path)}
form_widget_args = {
'is_main_image': {
'type': 'checkbox',
'class': 'flat-red'
},
}
def benchmark(parameter_name, values, config=CONFIG):
save_folder = generate_folder_name()
print("\n\nBenchmark for parameter '{}' with values {}".format(parameter_name, values))
results = np.empty((len(values), 2), dtype=float)
for index, v in enumerate(values):
print("Run {} with value {} on GPU".format(index+1, v))
results[index, 0] = run((parameter_name, v), ('gpu', True))
utils.clear_current_line()
print("Run {} with value {} on CPU".format(index+1, v))
results[index, 1] = run((parameter_name, v), ('gpu', False))
utils.clear_current_line()
result_str = "Results: \n{}".format(results)
print(result_str)
utils.save_dict(config, save_folder, 'config_' + parameter_name + '.txt')
utils.save_string_to_file(result_str, save_folder, 'results.txt')
def benchmark(parameter_name, values, config=CONFIG):
save_folder = generate_folder_name()
print("\n\nBenchmark for parameter '{}' with values {}".format(
parameter_name, values))
results = np.empty((len(values), 2), dtype=float)
for index, v in enumerate(values):
print("Run {} with value {} on GPU".format(index + 1, v))
results[index, 0] = run((parameter_name, v), ('gpu', True))
utils.clear_current_line()
print("Run {} with value {} on CPU".format(index + 1, v))
results[index, 1] = run((parameter_name, v), ('gpu', False))
utils.clear_current_line()
result_str = "Results: \n{}".format(results)
print(result_str)
utils.save_dict(config, save_folder, 'config_' + parameter_name + '.txt')
utils.save_string_to_file(result_str, save_folder, 'results.txt')
def parameter_tester(parameter_name, values, save_folder=None):
if save_folder is None:
save_folder = utils.generate_folder_name()
print("\n\nExperiment for parameter '{}' with values {}".format(
parameter_name, values))
plt.clf()
all_scores = []
for index, v in enumerate(values):
print("Run {} with value {}".format(index + 1, v))
scores = run((parameter_name, v))
all_scores.append(scores)
utils.print_on_current_line('')
header = ','.join(str(s) for s in values)
result_str = header + '\n' + ','.join(["{:.4f}".format(sum(s) / CLASSIFIER_CONFIG.runs) for s in all_scores]) + \
'\n\n' + 'all scores:\n'
for a in all_scores:
result_str += ','.join('{:.4f}'.format(s) for s in a) + '\n'
utils.save_string_to_file(result_str,
parent_folder=save_folder,
file_name='result_' + parameter_name + '.txt')
class RegistryView(MyModelView):
column_filters = ['is_active']
relative_file_path = get_relative_file_path('registries', generate_folder_name())
form_widget_args = {
'is_active': {
'type': 'checkbox',
'class': 'flat-red'
},
}
form_extra_fields = {
'image': form.ImageUploadField('Background Image', allowed_extensions=['jpg', 'jpeg', 'png'], base_path=get_file_path(),
relative_path=relative_file_path)
}
can_delete = False
@action('activate', 'Mark as Activated', 'Are you sure you want to mark these items as active?')
def action_activate(self, ids):
try:
count = self.model.query.filter(self.model.id.in_(ids)).update({self.model.is_active: True}, synchronize_session='fetch')
db.session.commit()
flash(f'{count} items were successfully marked as active', 'success')
except Exception as ex:
if not self.handle_view_exception(ex):
raise
flash(f'Failed to process request. {str(ex)}', 'error')
@action('deactivate', 'Mark as Deactivated', 'Are you sure you want to mark these items as deactivated?')
def action_deactivate(self, ids):
try:
count = self.model.query.filter(self.model.id.in_(ids)).update({self.model.is_active: False}, synchronize_session='fetch')
db.session.commit()
flash(f'{count} items were successfully marked as deactivated', 'success')
except Exception as ex:
if not self.handle_view_exception(ex):
raise
flash(f'Failed to process request. {str(ex)}', 'error')
def benchmark_cost_function(data_sizes):
polygon = pickle.load(open('utils/good_path_for_rectangle.pickle', 'rb'))
save_folder = generate_folder_name()
iterations = 10
results = np.empty((len(data_sizes), iterations, 2), dtype=float)
for i, dsize in enumerate(data_sizes):
data = dg.generate_rectangle_set(dsize)
print("\nRun {} with value {}".format(i + 1, dsize))
# Compile functions and warm up GPU
acoc.cost_function_gpu(data.T, polygon)
for j in range(iterations):
utils.print_on_current_line('Iteration {}/{}'.format(
j, iterations))
start_cpu = time.clock()
acoc.cost_function(data.T, polygon)
end_cpu = time.clock()
results[i][j][0] = end_cpu - start_cpu
start_gpu = time.clock()
acoc.cost_function_gpu(data.T, polygon)
end_gpu = time.clock()
results[i][j][2] = end_gpu - start_gpu
mean_results = np.mean(results, axis=1).T
acoc_plotter.plot_bar_chart_gpu_benchmark(mean_results, data_sizes,
['CPython', 'GPU'], save_folder,
'results')
np.set_printoptions(precision=7, suppress=False)
print("\nResults: \n{}".format(mean_results))
utils.save_object(mean_results, save_folder, 'results')
class ArticleView(MyModelView):
column_list = ['title', 'slug', 'summary', 'is_published', 'view_count', 'created_by', 'date_created']
column_labels = dict(is_published="Published?")
form_excluded_columns = ['slug', 'created_by', 'date_created', 'view_count']
form_widget_args = {
'content': {
'rows': 20,
'class': "form-control textarea"
},
'summary': {
'rows': 5,
'class': "form-control textarea"
},
'is_published': {
'type': 'checkbox',
'class': 'flat-red'
},
}
column_formatters = {
'summary': strip_html_tags,
'content': strip_html_tags,
}
folder_name = generate_folder_name()
relative_file_path = get_relative_file_path('articles', folder_name)
form_overrides = dict(image=FileField)
form_columns = ['title', 'summary', 'content', 'tags', 'image', 'is_published']
form_extra_fields = {
'image': form.ImageUploadField('Image', allowed_extensions=['jpg', 'jpeg', 'png'], base_path=get_file_path(),
relative_path=relative_file_path)}
def on_model_change(self, form, model, is_created):
if is_created:
model.created_by = current_user
unique_slug = UniqueSlugify(to_lower=True)
model.slug = unique_slug(model.title)
else:
slug = Slugify(to_lower=True)
model.slug = slug(model.title)
@action('publish', 'Mark as Published', 'Are you sure you want to publish selected articles?')
def action_publish(self, ids):
try:
count = Article.query.filter(Article.id.in_(ids)).update({Article.is_published: True},
synchronize_session='fetch')
db.session.commit()
flash(f'{count} articles were successfully published', 'success')
except Exception as ex:
if not self.handle_view_exception(ex):
raise
flash(f'Failed to publish articles. {str(ex)}', 'error')
@action('archive', 'Mark as Archived', 'Are you sure you want to archive selected articles?')
def action_archive(self, ids):
try:
count = Article.query.filter(Article.id.in_(ids)).update({Article.is_published: False},
synchronize_session='fetch')
db.session.commit()
flash(f'{count} articles were successfully archived', 'success')
except Exception as ex:
if not self.handle_view_exception(ex):
raise
flash(f'Failed to archive articles. {str(ex)}', 'error')
from copy import copy
from time import time
import acoc
import utils
from utils import data_manager, generate_folder_name
import acoc.polygon
from config import CLASSIFIER_CONFIG, SAVE_DIR
CLASSIFIER_CONFIG.plot = False
CLASSIFIER_CONFIG.save = False
CLASSIFIER_CONFIG.training_test_split = True
CLASSIFIER_CONFIG.data_set = 'iris'
SAVE_FOLDER = generate_folder_name(CLASSIFIER_CONFIG.data_set, SAVE_DIR)
def run(**kwargs):
conf = copy(CLASSIFIER_CONFIG)
for k, v in kwargs.items():
conf[k] = v
data_set = data_manager.load_data_set(conf.data_set)
X = data_set.data
y = data_set.target
class_indices = list(set(y))
# Split data into training and testing set
if conf.training_test_split:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
data_set = data_manager.load_data_set('breast_cancer')
data = data_set.data[:, :2]
target = np.array([data_set.target]).T
np.place(target, target == 4, 1)
np.place(target, target == 2, 0)
dt = np.concatenate((data, target), axis=1)
# dt = np.array([[0, 0.02, 0],
# [0.05, 0, 1],
# [1, 1, 1]])
mtrx = AcocMatrix(dt)
pol = mtrx.edges[:4]
mtrx.edges[1].pheromone_strength = 5
mtrx.edges[0].pheromone_strength = 3
save_folder = generate_folder_name()
print("Level {}: Edges {}, vertices {}".format(mtrx.level, len(mtrx.edges),
len(mtrx.vertices)))
if plot:
# plot_pheromones(mtrx, dt, tau_min=1, tau_max=10, folder_name=save_folder, save=save, show=show)
plot_matrix_and_data(mtrx, dt, show=show)
for i in range(6):
mtrx.level_up(pol)
print("Level {}: Edges {}, vertices {}".format(mtrx.level,
len(mtrx.edges),
len(mtrx.vertices)))
if plot:
plot_matrix_and_data(mtrx, dt, show=show, save=save)
# plot_pheromones(mtrx, dt, tau_min=1, tau_max=10, folder_name=save_folder, save=save, show=show)
def parameter_tester(parameter_name, values, save_folder=None):
if save_folder is None:
save_folder = utils.generate_folder_name()
print("\n\nExperiment for parameter '{}' with values {}".format(
parameter_name, values))
plt.clf()
all_scores = []
for index, v in enumerate(values):
print("Run {} with value {}".format(index + 1, v))
scores = run((parameter_name, v))
all_scores.append(scores)
utils.print_on_current_line('')
header = ','.join(str(s) for s in values)
result_str = header + '\n' + ','.join(["{:.4f}".format(sum(s) / CLASSIFIER_CONFIG.runs) for s in all_scores]) + \
'\n\n' + 'all scores:\n'
for a in all_scores:
result_str += ','.join('{:.4f}'.format(s) for s in a) + '\n'
utils.save_string_to_file(result_str,
parent_folder=save_folder,
file_name='result_' + parameter_name + '.txt')
if __name__ == "__main__":
folder = generate_folder_name('tuning-'.format(CLASSIFIER_CONFIG.data_set))
utils.save_dict(CLASSIFIER_CONFIG, folder, file_name='base_config.txt')
parameter_tester('rho', [0.02, 0.05, 0.07], save_folder=folder)
parameter_tester('beta', [0.02, 0.05, 0.07], save_folder=folder)
plot = True
save = True
show = False
data_set = data_manager.load_data_set('breast_cancer')
data = data_set.data[:, :2]
target = np.array([data_set.target]).T
np.place(target, target == 4, 1)
np.place(target, target == 2, 0)
dt = np.concatenate((data, target), axis=1)
# dt = np.array([[0, 0.02, 0],
# [0.05, 0, 1],
# [1, 1, 1]])
mtrx = AcocMatrix(dt)
pol = mtrx.edges[:4]
mtrx.edges[1].pheromone_strength = 5
mtrx.edges[0].pheromone_strength = 3
save_folder = generate_folder_name()
print("Level {}: Edges {}, vertices {}".format(mtrx.level, len(mtrx.edges), len(mtrx.vertices)))
if plot:
# plot_pheromones(mtrx, dt, tau_min=1, tau_max=10, folder_name=save_folder, save=save, show=show)
plot_matrix_and_data(mtrx, dt, show=show)
for i in range(6):
mtrx.level_up(pol)
print("Level {}: Edges {}, vertices {}".format(mtrx.level, len(mtrx.edges), len(mtrx.vertices)))
if plot:
plot_matrix_and_data(mtrx, dt, show=show, save=save)
# plot_pheromones(mtrx, dt, tau_min=1, tau_max=10, folder_name=save_folder, save=save, show=show)
