class LearningAgentTest( unittest.TestCase ):
def setUp( self ):
'''Set up the dependencies for the test execution'''
# Construct an InputReader and FeatureExtractor for dependency injection
self.mInputReader = InputReader( testFile )
self.mFeatureExtractor = LendingClubFeatureExtractor( self.mInputReader,
filterFile )
# Push our local test data into the FeatureExtractor
self.mFeatureExtractor.setTrainingData( g_testArray )
# Now, construct the class under test with the FeatureExtractor
self.mLearningAgent = DummyLearningAgentImpl( self.mFeatureExtractor )
def test_getTrainingData( self ):
'''Test getTrainingData() function returns correct data'''
np.testing.assert_array_equal( self.mLearningAgent.getTrainingData(),
g_testArray )
def test_sampleSlice( self ):
'''Test sampleSlice() function correctly splits test and train data'''
# Configure the data to be split evenly for the test
self.mLearningAgent.sampleSlice( 0.5 )
# Get the target feature index
m_yIdx = self.mFeatureExtractor.listIdx( 'loan_status' )
# Slice boundary
mBnd = ceil( len(g_testArray) / 2 )
# Generate X_train test array checksum
m_X_train_sum = np.sum( g_testArray[:mBnd] )
m_X_train_sum = m_X_train_sum - np.sum( g_testArray[:mBnd, m_yIdx] )
# Verify sums match w/in some small tolerance
self.assertTrue( fabs( m_X_train_sum -
np.sum( self.mLearningAgent.X_train ) < 0.001 ) )
# Generate X_test test array checksum
m_X_test_sum = np.sum( g_testArray[mBnd:] )
m_X_test_sum = m_X_test_sum - np.sum( g_testArray[mBnd:, m_yIdx] )
# Verify sums match w/in some small tolerance
self.assertTrue( fabs( m_X_test_sum -
np.sum( self.mLearningAgent.X_test ) < 0.001 ) )
# Generate y_train array checksum
m_y_train_sum = np.sum( g_testArray[:mBnd, m_yIdx] )
# Verify sums match w/in some small tolerance
self.assertTrue( fabs( m_y_train_sum -
np.sum( self.mLearningAgent.y_train ) < 0.001 ) )
# Generate y_test array checksum
m_y_test_sum = np.sum( g_testArray[mBnd:, m_yIdx] )
# Verify sums match w/in some small tolerance
self.assertTrue( fabs( m_y_test_sum -
np.sum( self.mLearningAgent.y_test ) < 0.001 ) )
def test_standardizeSamples( self ):
'''
Test standardizeSamples() function calculates mean and deviation and
applies it properly to all samples of a given feature
Note: Just testing the first feature here
'''
# Configure the data to be split evenly for the test
self.mLearningAgent.sampleSlice( 0.5 )
# Calculate average and standard deviation
mSum = sum( self.mLearningAgent.X_train[:, 0] )
mAvg = mSum / len( self.mLearningAgent.X_train[:, 0] )
mStdDev = sum( np.square( np.subtract(
self.mLearningAgent.X_train[:, 0], mAvg ) ) )
mStdDev = sqrt( fabs(
mStdDev / len( self.mLearningAgent.X_train[:, 0] ) ) )
# Apply calculated average and standard deviation to samples
mNorm = np.divide( np.subtract( self.mLearningAgent.X_train[:, 0],
mAvg ), mStdDev )
# Execute LearningAgent implementation
self.mLearningAgent.standardizeSamples()
# Assert local calculation matches with LearningAgent implementation
self.assertTrue( fabs( sum(
np.subtract( mNorm, self.mLearningAgent.X_train[:, 0] ) ) )
< 0.001 )
def test_shuffleSamples( self ):
'''Test shuffleSamples() function shuffles samples correctly'''
# Initialize random number generator w/ known seed
np.random.seed( 1 )
# Generate index list
m_indices = np.random.permutation( len( g_testArray ) )
# Make LearningAgent call w/ the same seed
self.mLearningAgent.shuffleSamples( 1 )
# Assert data is shuffled as expected
np.testing.assert_array_equal( g_testArray[m_indices],
self.mLearningAgent.trainingData )
