class LendingClubFeatureExtractorTest(unittest.TestCase):
def setUp(self):
"""Set up the dependencies for the test execution"""
# Construct our InputReader object, pass it the test csv file
self.mInputReader = InputReader(testFile)
# Construct the class under test with the InputReader
self.mFeatureExtractor = LendingClubFeatureExtractor(self.mInputReader, filterTestFile)
def test_termConversion(self):
"""Test termEnumerator functionality"""
# Grab appropriate column index
idx = self.mFeatureExtractor.listIdx("term")
# Loop over all test data and assert proper enumeration
for row in self.mFeatureExtractor.getTrainingData():
term = self.mFeatureExtractor.termConversion(row)
if re.search("36 months", row[idx]):
self.assertEqual(36, term)
elif re.search("60 months", row[idx]):
self.assertEqual(60, term)
else:
raise ValueError("Encountered unsupported term value")
def test_pcntRemove(self):
"""Test '%' removal"""
# Loop over all test data and assert proper conversion
for row in self.mFeatureExtractor.getTrainingData():
int_rate = self.mFeatureExtractor.pcntRemove(row, "int_rate")
revol_util = self.mFeatureExtractor.pcntRemove(row, "revol_util")
# Assert that no '%' contained in the results
self.assertFalse(re.search("%", str(int_rate)))
self.assertFalse(re.search("%", str(revol_util)))
def test_loanGradeHash(self):
"""Test loan grade hashing function"""
# Grab appropriate column index
idx = self.mFeatureExtractor.listIdx("sub_grade")
# Hardcode test dictionary
mTestDict = {
"A1": 1,
"A2": 2,
"A3": 3,
"A4": 4,
"A5": 5,
"B1": 6,
"B2": 7,
"B3": 8,
"B4": 9,
"B5": 10,
"C1": 11,
"C2": 12,
"C3": 13,
"C4": 14,
"C5": 15,
"D1": 16,
"D2": 17,
"D3": 18,
"D4": 19,
"D5": 20,
"E1": 21,
"E2": 22,
"E3": 23,
"E4": 24,
"E5": 25,
"F1": 26,
"F2": 27,
"F3": 28,
"F4": 29,
"F5": 30,
"G1": 31,
"G2": 32,
"G3": 33,
"G4": 34,
"G5": 35,
}
# Loop over all test data and assert correct hash is returned
for row in self.mFeatureExtractor.getTrainingData():
sub_grade_hash = self.mFeatureExtractor.loanGradeHash(row)
testKey = row[idx]
self.assertEqual(mTestDict[testKey], sub_grade_hash)
def test_empLengthConversion(self):
"""Test employment length function"""
# Grab appropriate column index
idx = self.mFeatureExtractor.listIdx("emp_length")
# Loop over all test data and assert correct conversion is returned
for row in self.mFeatureExtractor.getTrainingData():
emp_length = self.mFeatureExtractor.empLengthConversion(row)
# Convert function calculated text back to expected string
if emp_length == 0.1:
emp_length = "<"
elif emp_length == 20:
emp_length = "10"
elif emp_length == 0:
emp_length = "n/a"
else:
emp_length = str(emp_length)
# Use converted emp_length for reg exp test against test resource
self.assertTrue(re.search(emp_length, row[idx]))
def test_homeOwnershipEnumerator(self):
"""Test home ownership enumeration"""
# Grab appropriate column index
idx = self.mFeatureExtractor.listIdx("home_ownership")
# Test dictionary
mTestDict = {1: "RENT", 2: "MORTGAGE", 3: "OWN", 4: "OTHER"}
# Loop over all test data and assert correct enumeration is returned
for row in self.mFeatureExtractor.getTrainingData():
homeOwnE = self.mFeatureExtractor.homeOwnershipEnumerator(row)
# Convert function result back to expected string
if homeOwnE == 1 or 2 or 3 or 4:
homeOwn = mTestDict[homeOwnE]
else:
homeOwn = "FAIL"
# Use converted homeOwn for reg exp test against test resource
self.assertTrue(re.search(homeOwn, row[idx]))
def test_incomeVerifiedConversion(self):
"""Test income verification conversion"""
# Grab appropriate column index
idx = self.mFeatureExtractor.listIdx("is_inc_v")
# Loop over all test data and assert correct conversion is returned
for row in self.mFeatureExtractor.getTrainingData():
is_inc_v = self.mFeatureExtractor.incomeVerifiedConversion(row)
# Assert that when is_inc_v is 0, input contains 'Not' string
if is_inc_v == 0:
self.assertTrue(re.search("Not", row[idx]))
else:
self.assertFalse(re.search("Not", row[idx]))
def test_purposeEnumerator(self):
"""Test loan purpose enumeration"""
# Grab appropriate column index
idx = self.mFeatureExtractor.listIdx("purpose")
# Test dictionary - This must align with UUT dict!!
mTestDict = {
1: "house",
2: "home_improvement",
3: "medical",
4: "education",
5: "debt_consolidation",
7: "small_business",
8: "major_purchase",
9: "car",
10: "credit_card",
11: "wedding",
12: "vacation",
}
# Loop over all test data and assert correct conversion is returned
for row in self.mFeatureExtractor.getTrainingData():
purpose = self.mFeatureExtractor.purposeEnumerator(row)
# Assert string is found in test data based on returned enum
if purpose in mTestDict.keys():
self.assertEqual(mTestDict[purpose], re.search(mTestDict[purpose], row[idx]).group())
def test_stateEnumerator(self):
"""Test state enumeration"""
# Grab appropriate column index
idx = self.mFeatureExtractor.listIdx("addr_state")
# Test dictionary - This must align with UUT dict!!
mTestDict = {
1: "AK",
2: "AL",
3: "AR",
4: "AZ",
5: "CA",
6: "CO",
7: "CT",
8: "DC",
9: "DE",
10: "FL",
11: "GA",
12: "HI",
13: "IA",
14: "ID",
15: "IL",
16: "IN",
17: "KS",
18: "KY",
19: "LA",
20: "MA",
21: "MD",
22: "ME",
23: "MI",
24: "MN",
25: "MO",
26: "MS",
27: "MT",
28: "NC",
29: "ND",
30: "NE",
31: "NH",
32: "NJ",
33: "NM",
34: "NV",
35: "NY",
36: "OH",
37: "OK",
38: "OR",
39: "PA",
40: "PR",
41: "RI",
42: "SC",
43: "SD",
44: "TN",
45: "TX",
46: "UT",
47: "VA",
48: "VI",
49: "VT",
50: "WA",
51: "WI",
52: "WV",
53: "WY",
}
# Loop over all test data and assert correct conversion is returned
for row in self.mFeatureExtractor.getTrainingData():
addr_stateE = self.mFeatureExtractor.stateEnumerator(row)
# Assert returned value matches w/ test dictionary
self.assertTrue(re.search(mTestDict[addr_stateE], row[idx]))
def test_earlyCrLineConversion(self):
"""Test date conversion"""
# Grab appropriate column index
idx = self.mFeatureExtractor.listIdx("earliest_cr_line")
# Generate a test date
testDate = ["01/01/1972 01:50"]
# Time elapsed since 2014
delta = 42
# Push null entries into testDate to simulate feature placement in
# the training set
for i in range(idx):
testDate.insert(0, "")
# Assert time elapsed since epoch is correct for given test time
self.assertEqual(delta, self.mFeatureExtractor.earlyCrLineConversion(testDate))
def test_statusConversion(self):
"""Loan status conversion test"""
# Grab appropriate column index
idx = self.mFeatureExtractor.listIdx("loan_status")
# Loop over all test data and assert correct conversion is returned
for row in self.mFeatureExtractor.getTrainingData():
loan_status = self.mFeatureExtractor.statusConversion(row)
# Assert that correct loan_status is returned based on test input
if loan_status == 0:
self.assertTrue(re.search("Charged Off", row[idx]))
elif loan_status == 1:
self.assertTrue(re.search("Fully Paid", row[idx]))
else:
self.assertFalse(re.search("Fully Paid|Charged Off", row[idx]))
def test_extractFeatures(self):
"""Feature extraction test"""
# Get initial training sample count
nSamples = self.mFeatureExtractor.getSampleCnt()
# Invoke feature extraction on our test object
self.mFeatureExtractor.extractFeatures()
# Make local sample removal count
nRmvSamples = nSamples - self.mFeatureExtractor.getSampleCnt()
# Assert local removal calculation corresponds with actual
self.assertEqual(nRmvSamples, self.mFeatureExtractor.getRmvSampleCnt())
