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(