def doit(predictions, true_labels, expected):
"""Runs a single test case
Parameters
==========
predictions : list
A list of integer predictions to input
true_labels : list
Ground truth values to compare to
expected : float
The expected classification-error rate
Raises
======
AssertionError
In case something goes wrong
"""
predictions = numpy.array(predictions)
true_labels = numpy.array(true_labels)
cer = analysis.CER(predictions, true_labels)
assert numpy.isclose(cer, expected), "Expected %r, but got %r" % (
expected,
cer,
)
def test_one(protocol, variables):
train = database.get(protocol, 'train', database.CLASSES, variables)
norm = preprocessor.estimate_norm(numpy.vstack(train))
train_normed = preprocessor.normalize(train, norm)
trainer = algorithm.MultiClassTrainer()
machine = trainer.train(train_normed)
test = database.get(protocol, 'test', database.CLASSES, variables)
test_normed = preprocessor.normalize(test, norm)
test_predictions = machine.predict(numpy.vstack(test_normed))
test_labels = algorithm.make_labels(test).astype(int)
return analysis.CER(test_predictions, test_labels)
def test_one(protocol, variables):
"""Runs one single test, returns the CER on the test set"""
# 1. get the data from our preset API for the database
train = database.get(protocol, "train", database.CLASSES, variables)
# 2. preprocess the data using our module preprocessor
norm = preprocessor.estimate_norm(numpy.vstack(train))
train_normed = preprocessor.normalize(train, norm)
# 3. trains our logistic regression system
trainer = algorithm.MultiClassTrainer()
machine = trainer.train(train_normed)
# 4. applies the machine to predict on the 'unseen' test data
test = database.get(protocol, "test", database.CLASSES, variables)
test_normed = preprocessor.normalize(test, norm)
test_predictions = machine.predict(numpy.vstack(test_normed))
test_labels = algorithm.make_labels(test).astype(int)
return analysis.CER(test_predictions, test_labels)
def doit(predictions, true_labels, expected):
'''Runs a single test case
Parameters:
predictions (list): A list of integer predictions to input
true_labels (list): Ground truth values to compare to
expected (float): The expected classification-error rate
Raises:
AssertionError: in case something goes wrong
'''
predictions = numpy.array(predictions)
true_labels = numpy.array(true_labels)
cer = analysis.CER(predictions, true_labels)
assert numpy.isclose(cer,
expected), 'Expected %r, but got %r' % (expected, cer)
def doit(predictions, true_labels, expected): predictions = numpy.array(predictions) true_labels = numpy.array(true_labels) cer = analysis.CER(predictions, true_labels) assert numpy.isclose(cer, expected), 'Expected %r, but got %r' % (expected, cer)