class ELMClassifiertest(unittest.TestCase):
def setUp(self):
self.mockConnectionPool = Mock(spec=pycassa.pool.ConnectionPool)
self.minimizationType = 'FP'
self.numberNeurons = 10
np.random.seed(1)
@patch('elm.pycassa.columnfamily.ColumnFamily')
def test_train_wisconsin1(self, mockFunction):
print 'Yes, I am running, be patient, Human.'
returnedMock = Mock(spec=pycassa.ColumnFamily)
returnedMock.get = mock_columnfamilyget
returnedMock.batch = Mock()
mockbatchinsert = Mock()
mockbatchinsert.insert = mock_batchinsert
mockbatch = Mock()
mockbatch.__enter__ = Mock(return_value=mockbatchinsert)
mockbatch.__exit__ = Mock(return_value=False)
mockFunction.return_value = returnedMock
returnedMock.batch.return_value = mockbatch
self.myELM = ELM(self.minimizationType, self.mockConnectionPool, self.numberNeurons)
inputData = np.loadtxt('./test_train_input_wisconsin1.txt').reshape((379, 30))
outputData = np.loadtxt('./test_train_output_wisconsin1.txt').reshape((379, 1))
self.myELM.ELMTrainingData = ELMData(inputData, outputData, [], [])
self.myELM.storeTrainingDataToCassandra()
self.myELM.performTraining()
self.myELM.trained = True
print 'Best LOO result retained: %s' % self.myELM.bestClassificationRateLOO
inputTestData = np.loadtxt('./test_test_input_wisconsin1.txt').reshape((190, 30))
outputTestData = np.loadtxt('./test_test_output_wisconsin1.txt').reshape((190, 1))
testData = ELMData(inputTestData, outputTestData, [], [])
confidence, verdict = self.myELM.verdict(testData)
print 'Test error: %s' % np.mean(verdict == outputTestData)
listOfErrors = [item for item in xrange(len(verdict)) if verdict[item]!=outputTestData[item]]
print confidence[listOfErrors]
