def test_cross_validation_split_all_data__no_output_data_lost(self):
from main import convert_data_to_arrays, get_rdf_data, cross_validation_split_all_data
KB, supports, outputs, num1, num2 = convert_data_to_arrays(get_rdf_data('rdfData/gfo-1.0.json'))
# Processes data.
KBs_tests, KBs_trains, X_trains, X_tests, y_trains, y_tests = cross_validation_split_all_data(7, KB, supports, outputs)
expected = []
for sample in outputs:
expected.append(sample.tolist())
padding_count = 0
for sample in expected:
if len(sample[0]) > padding_count:
padding_count = len(sample[0])
for sampleNum in range(len(expected)):
for timestepNum in range(len(expected[sampleNum])):
while len(expected[sampleNum][timestepNum]) < padding_count:
expected[sampleNum][timestepNum].append(0.0)
trains = y_trains[0].tolist()
trains.extend(y_tests[0].tolist())
actual = trains
for sample in range(len(expected)):
for t_sample in range(len(actual)):
if expected[sample] == actual[t_sample]:
expected[sample] = None
actual[t_sample] = None
break
self.assertEqual(actual, expected)
def test_get_labels_from_encoding__handles_true_and_pred_values_the_same(self):
from main import get_labels_from_encoding, convert_data_to_arrays, get_rdf_data, cross_validation_split_all_data
KB, supports, outputs, numConcepts, numRoles = convert_data_to_arrays(get_rdf_data('rdfData/gfo-1.0.json'))
allTheData = cross_validation_split_all_data(5, KB, supports, outputs)
KBs_tests, KBs_trains, X_trains, X_tests, y_trains, y_tests = allTheData
trueArr, predArr = get_labels_from_encoding(y_tests[0], y_tests[0], 28, 14)
self.assertEqual(trueArr.all(), predArr.all())
def test_cross_validation_split_all_data__correct_mapping_fromKBToSuppToOuts_of_test(self):
from main import convert_data_to_arrays, get_rdf_data, \
cross_validation_split_all_data
KB, supports, outputs, num1, num2 = convert_data_to_arrays(get_rdf_data('rdfData/gfo-1.0.json'))
# Processes data.
KBs_tests, KBs_trains, X_trains, X_tests, y_trains, y_tests = cross_validation_split_all_data(3, KB, supports, outputs)
trueKB = KB.tolist()
crossKB = KBs_tests[0].tolist()
# Outputs ----------------------
trueOuts = []
for sample in outputs:
trueOuts.append(sample.tolist())
padding_count = 0
for sample in trueOuts:
if len(sample[0]) > padding_count:
padding_count = len(sample[0])
for sampleNum in range(len(trueOuts)):
for timestepNum in range(len(trueOuts[sampleNum])):
while len(trueOuts[sampleNum][timestepNum]) < padding_count:
trueOuts[sampleNum][timestepNum].append(0.0)
actualOuts = y_tests[0].tolist()
# Supports ---------------------
trueSupp = []
for sample in supports:
trueSupp.append(sample.tolist())
padding_count = 0
for sample in trueSupp:
if len(sample[0]) > padding_count:
padding_count = len(sample[0])
for sampleNum in range(len(trueSupp)):
for timestepNum in range(len(trueSupp[sampleNum])):
while len(trueSupp[sampleNum][timestepNum]) < padding_count:
trueSupp[sampleNum][timestepNum].append(0.0)
actualSupp = X_tests[0].tolist()
for sample in range(len(crossKB)):
for t_sample in range(len(trueKB)):
if crossKB[sample][0] == trueKB[t_sample]:
self.assertTrue(actualOuts[sample] == trueOuts[t_sample])
self.assertTrue(actualSupp[sample] == trueSupp[t_sample])
def test_cross_validation_split_all_data__correct_numOf_folds_returned(self):
from main import convert_data_to_arrays, get_rdf_data, cross_validation_split_all_data
KB, supports, outputs, num1, num2 = convert_data_to_arrays(get_rdf_data('rdfData/gfo-1.0.json'))
# Processes data.
KBs_tests, KBs_trains, X_trains, X_tests, y_trains, y_tests = cross_validation_split_all_data(7, KB, supports, outputs)
self.assertEqual(7, KBs_tests.shape[0])
self.assertEqual(7, KBs_trains.shape[0])
self.assertEqual(7, X_tests.shape[0])
self.assertEqual(7, X_trains.shape[0])
self.assertEqual(7, y_tests.shape[0])
self.assertEqual(7, y_tests.shape[0])
def test_cross_validation_split_all_data__correct_KB_repetition(self):
from main import convert_data_to_arrays, get_rdf_data, cross_validation_split_all_data
KB, supports, outputs, num1, num2 = convert_data_to_arrays(get_rdf_data('rdfData/gfo-1.0.json'))
# Processes data.
KBs_tests, KBs_trains, X_trains, X_tests, y_trains, y_tests = cross_validation_split_all_data(8, KB, supports, outputs)
tests = KBs_tests.tolist()
for cross in tests:
for sample in cross:
standard = sample[0]
for ts in sample:
self.assertTrue(standard == ts)
trains = KBs_trains.tolist()
for cross in trains:
for sample in cross:
standard = sample[0]
for ts in sample:
self.assertTrue(standard == ts)
def test_get_labels_from_encoding__each_sample_has_correct_size(self):
from main import get_labels_from_encoding, convert_data_to_arrays, get_rdf_data, cross_validation_split_all_data
KB, supports, outputs, numConcepts, numRoles = convert_data_to_arrays(get_rdf_data('rdfData/gfo-1.0.json'))
allTheData = cross_validation_split_all_data(5, KB, supports, outputs)
KBs_tests, KBs_trains, X_trains, X_tests, y_trains, y_tests = allTheData
for i in range(len(y_tests)):
trueArr, predArr = get_labels_from_encoding(y_tests[i], y_tests[i], 28, 14)
self.assertEqual(trueArr.all(), predArr.all())
s = y_tests[i].shape
s1 = trueArr.shape
s2 = predArr.shape
self.assertEqual((s[0],s[1]), s1, s2)
for sample in range(len(trueArr)):
for ts in range(len(trueArr[sample])):
self.assertEqual(len(trueArr[sample][ts]), s[2]/3)
self.assertEqual(len(predArr[sample][ts]), s[2] / 3)
def test_cross_validation_split_all_data__no_kb_data_lost(self):
from main import convert_data_to_arrays, get_rdf_data, cross_validation_split_all_data
KB, supports, outputs, numConcepts, numRoles = convert_data_to_arrays(get_rdf_data('rdfData/gfo-1.0.json'))
# Processes data.
KBs_tests, KBs_trains, X_trains, X_tests, y_trains, y_tests = cross_validation_split_all_data(5, KB, supports, outputs)
expected = KB.tolist()
trains = KBs_trains[0].tolist()
trains.extend(KBs_tests[0].tolist())
actual = trains
for sample in range(len(actual)):
actual[sample] = actual[sample][0]
for sample in range(len(expected)):
for t_sample in range(len(actual)):
if expected[sample] == actual[t_sample]:
expected[sample] = None
actual[t_sample] = None
break
self.assertEqual(actual, expected)