def test_preformat_value(self):
def my_func():
pass
class MyClass(object):
pass
self.assertEqual('my_func', preformat_value(my_func))
self.assertEqual('MyClass', preformat_value(MyClass))
expected = ['my_func', 'MyClass', 1]
actual = preformat_value((my_func, MyClass, 1))
np.testing.assert_array_equal(expected, actual)
expected = ['my_func', 'MyClass', 1]
actual = preformat_value([my_func, MyClass, 1])
np.testing.assert_array_equal(expected, actual)
expected = sorted(['my_func', 'MyClass', 'x'])
actual = sorted(preformat_value({my_func, MyClass, 'x'}))
np.testing.assert_array_equal(expected, actual)
self.assertEqual(1, preformat_value(1))
expected = (3, 2)
actual = preformat_value(np.ones((3, 2)))
np.testing.assert_array_equal(expected, actual)
expected = (1, 2)
actual = preformat_value(np.matrix([[1, 1]]))
np.testing.assert_array_equal(expected, actual)
def test_preformat_value(self):
def my_func():
pass
class MyClass(object):
pass
self.assertEqual('my_func', preformat_value(my_func))
self.assertEqual('MyClass', preformat_value(MyClass))
expected = ['my_func', 'MyClass', 1]
actual = preformat_value((my_func, MyClass, 1))
np.testing.assert_array_equal(expected, actual)
expected = ['my_func', 'MyClass', 1]
actual = preformat_value([my_func, MyClass, 1])
np.testing.assert_array_equal(expected, actual)
expected = sorted(['my_func', 'MyClass', 'x'])
actual = sorted(preformat_value({my_func, MyClass, 'x'}))
np.testing.assert_array_equal(expected, actual)
self.assertEqual(1, preformat_value(1))
expected = (3, 2)
actual = preformat_value(np.ones((3, 2)))
np.testing.assert_array_equal(expected, actual)
expected = (1, 2)
actual = preformat_value(np.matrix([[1, 1]]))
np.testing.assert_array_equal(expected, actual)
def repr_options(self):
options = []
for option_name in self.options:
option_value = getattr(self, option_name)
option_value = preformat_value(option_value)
option_repr = "{}={}".format(option_name, option_value)
options.append(option_repr)
return ', '.join(options)
def _repr_options(self):
options = []
for option_name in self.options:
option_value = getattr(self, option_name)
option_value = preformat_value(option_value)
option_repr = "{}={}".format(option_name, option_value)
options.append(option_repr)
return ', '.join(options)
def logging_info_about_the_data(network, input_train, input_test):
logs = network.logs
training_shapes = preformat_value(input_train)
logs.title("Start training")
logs.message("TRAINING DATA", "shapes: {}".format(training_shapes))
if isinstance(training_shapes[0], int):
training_shapes = [training_shapes]
if input_test is not None:
test_shapes = preformat_value(input_test)
logs.message("TEST DATA", "shapes: {}".format(test_shapes))
if isinstance(test_shapes[0], int):
test_shapes = [test_shapes]
for training_shape, test_shape in zip(training_shapes, test_shapes):
if training_shape[1:] != test_shape[1:]:
raise ValueError(
"Train and test samples should have the same feature "
"shapes. Got training input with shape {} and test "
"input with shape {}".format(training_shape, test_shape))
def show_network_options(network, highlight_options=None):
""" Display all available parameters options for Neural Network.
Parameters
----------
network : object
Neural network instance.
highlight_options : list
List of enabled options. In that case all options from that
list would be marked with a green color.
"""
available_classes = [cls.__name__ for cls in network.__class__.__mro__]
logs = network.logs
if highlight_options is None:
highlight_options = {}
def group_by_class_name(value):
_, option = value
option_priority = -available_classes.index(option.class_name)
return option_priority, option.class_name
grouped_options = groupby(
sorted(network.options.items(), key=group_by_class_name),
group_by_class_name,
)
logs.title("Main information")
logs.message("ALGORITHM", network.class_name())
logs.newline()
for (_, class_name), options in grouped_options:
if not options:
continue
logs.write("{}:".format(class_name))
for key, data in sorted(options):
if key in highlight_options:
msg_color = 'green'
value = highlight_options[key]
else:
msg_color = 'gray'
value = data.value
formated_value = preformat_value(value)
msg_text = "{} = {}".format(key, formated_value)
logs.message("OPTION", msg_text, color=msg_color)
logs.newline()
def __str__(self):
return str(preformat_value(self.default))
def __str__(self):
return str(preformat_value(self.default))