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#!/usr/bin/env python
"""General test for the NeuralNetwork libraries.
This exercises various elements of the BackPropagation NeuralNetwork
libraries.
"""
# standard library
import sys
import os
import random
# PyUnit
import unittest
# local stuff
from Bio.NeuralNetwork.Training import TrainingExample, ExampleManager
from Bio.NeuralNetwork.StopTraining import ValidationIncreaseStop
def run_tests(argv):
test_suite = testing_suite()
runner = unittest.TextTestRunner(sys.stdout, verbosity = 2)
runner.run(test_suite)
def testing_suite():
"""Generate the set of tests.
"""
test_suite = unittest.TestSuite()
test_loader = unittest.TestLoader()
test_loader.testMethodPrefix = 't_'
tests = [StopTrainingTest, ExampleManagerTest]
for test in tests:
cur_suite = test_loader.loadTestsFromTestCase(test)
test_suite.addTest(cur_suite)
return test_suite
class StopTrainingTest(unittest.TestCase):
"""Test functionality for stopping training networks.
"""
def t_validation_increase_stop(self):
"""Stop training when the ValidationExamples increase.
"""
stopper = ValidationIncreaseStop(max_iterations = 20,
min_iterations = 2)
stopper.last_error = 1.0
do_stop = stopper.stopping_criteria(5, 1.0, 1.5)
assert do_stop == 1, \
"Did not tell us to stop when validation error increased."
stopper.last_error = 1.0
do_stop = stopper.stopping_criteria(1, 1.0, 1.5)
assert do_stop == 0, \
"Told us to stop before we reached the minimum iterations."
stopper.last_error = 1.0
do_stop = stopper.stopping_criteria(25, 1.0, 0.5)
assert do_stop == 1, \
"Did not tell us to stop when reaching maximum iterations."
class ExampleManagerTest(unittest.TestCase):
"""Tests to make sure the example manager is working properly.
"""
def setUp(self):
self.num_examples = 500
self.examples = []
for make_example in range(self.num_examples):
inputs = []
for input_make in range(3):
inputs.append(random.randrange(1, 7))
outputs = [random.randrange(1, 7)]
self.examples.append(TrainingExample(inputs, outputs))
def t_adding_examples(self):
"""Make sure test examples are added properly.
"""
manager = ExampleManager()
# figure out the expected number of examples in each category
expected_train = manager.training_percent * self.num_examples
expected_validation = manager.validation_percent * self.num_examples
expected_test = self.num_examples - expected_train \
- expected_validation
manager.add_examples(self.examples)
for expect, actual in [(expected_train, len(manager.train_examples)),
(expected_validation,
len(manager.validation_examples)),
(expected_test, len(manager.test_examples))]:
wrong_percent = abs(expect - actual) / self.num_examples
assert wrong_percent < .1, \
"Deviation in how examples were added, expect %s, got %s" \
% (expect, actual)
def t_partioning_examples(self):
"""Test that we can change how to partition the test examples.
"""
manager = ExampleManager(0, 0)
manager.add_examples(self.examples)
assert len(manager.test_examples) == self.num_examples, \
"Did not partion correctly to test_examples."
manager = ExampleManager(1.0, 0)
manager.add_examples(self.examples)
assert len(manager.train_examples) == self.num_examples, \
"Did not partition correctly to train_examples."
manager = ExampleManager(0, 1.0)
manager.add_examples(self.examples)
assert len(manager.validation_examples) == self.num_examples, \
"Did not partition correctly to validation_examples."
if __name__ == "__main__":
sys.exit(run_tests(sys.argv))
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