def test_q_calculation(self):
dataRetriever = DataRetriever("../Datasets/metadata.json")
dataRetriever.retrieveData("breastCancer")
naiveBayes = NaiveBayes(dataRetriever.getDataSet(),
dataRetriever.getDataClass())
#seperatedByClass = naiveBayes.calculateQ()
#print(seperatedByClass)
self.assertEqual(
dataRetriever.getDataMenu(),
["breastCancer", "glass", "iris", "soybeanSmall", "vote"],
"should return list of data sets")
def test_menu(self):
dataRetriever = DataRetriever("../Datasets/metadata.json")
self.assertEqual(
dataRetriever.getDataMenu(),
["breastCancer", "glass", "iris", "soybeanSmall", "vote"],
"should return list of data sets")
else:
f += 1
print("The Percent of Correct Predictions is {t}%".format(
t=round((t * 100 / len(answers)), 1)))
print("The Percent of Incorrect Predictions is {f}%\n".format(
f=round((f * 100 / len(answers)), 1)))
dataRetriever = DataRetriever("../Datasets/metadata.json")
################################################ Un-Shuffled Data ################################################
# This first for loop performs the NaiveBayes algorithm for un-shuffled data
jsonResults1 = {}
for dataSet in dataRetriever.getDataMenu():
dataRetriever.retrieveData(dataSet)
dataClass = dataRetriever.getDataClass()
retrievedData = dataRetriever.getDataSet()
numOfClassValues = len(
retrievedData[dataRetriever.getDataClass()].unique())
method = "macro"
foldNum = 1
jsonResults1[dataSet] = {}
print(f"PRINTING RESULTS FOR THE CONTROL DATASET {dataSet}")
for train, test in KFolds(retrievedData, 10):
trainBin = BinDiscretizer(