def TestVocabulary(self):
#testingPara = testingParaHarryPotter
testingPara = self._TestingPara
passedTests = []
nonFatalTests = []
failedTests = []
# Build a test sequence form each word
for index, val in enumerate(self._TestingParaNlo.sentenceTokenList[1:]):
prevWord = self._TestingParaNlo.sentenceTokenList[index-1][0]
prevWordToken = self._TestingParaNlo.sentenceTokenList[index-1][1]
prevWordTokenNormal = self._TestingParaNlo.sentenceNormalised[index-1]
curWord = val[0]
curToken = val[1]
curNormal = self._TestingParaNlo.sentenceNormalised[index]
prediction = self.neuralNetworkV.getPredictedWord(prevWordTokenNormal, curToken)
probList = self.neuralNetworkV.getPredictionProbability(prevWordTokenNormal, curToken)
prob = 0
for val in probList[0]:
if(val > prob):
prob = val
if(str(curWord.lower()) == str(prediction).lower()):
passedTests.append("("+str(prevWord)+", "+str(prevWordToken)+") Target: "+str(curWord)+" Pred: "+str(prediction)+" " + str(prob*100) + "%")
else:
if(prob < 0.2):
failedTests.append("("+str(prevWord)+", "+str(prevWordToken)+") Target: "+str(curWord)+" Pred: "+str(prediction)+" " + str(prob*100) + "%")
elif (prob > 0.6):
passedTests.append("("+str(prevWord)+", "+str(prevWordToken)+") Target: "+str(curWord)+" Pred: "+str(prediction)+" " + str(prob*100) + "%")
else:
nonFatalTests.append("("+str(prevWord)+", "+str(prevWordToken)+") Target: "+str(curWord)+" Pred: "+str(prediction)+" " + str(prob*100) + "%")
# print results
print("\n")
print("********** TestSentenceStructuring() **********")
print("\n")
ConsoleOutput.printUnderline("Failed Tests: (" + str(len(failedTests)) + "/" + str(len(testingPara)) + ")")
for val in failedTests:
ConsoleOutput.printRed(val)
print("\n")
ConsoleOutput.printUnderline("Non-Fatal failed Tests: (" + str(len(nonFatalTests)) + "/" + str(len(testingPara)) + ")")
for val in nonFatalTests:
ConsoleOutput.printYellow(val)
print("\n")
ConsoleOutput.printUnderline("Passed Tests: (" + str(len(passedTests)) + "/" + str(len(testingPara)) + ")")
for val in passedTests:
ConsoleOutput.printGreen(val)
print("\n")
ConsoleOutput.printYellow("Passed: " + str(len(passedTests)) + " Non-Fatals: " + str(len(nonFatalTests)) + " Fails: " + str(len(failedTests)))
print("\n")
def TestVocabulary(self):
#testingPara = testingParaHarryPotter
testingPara = self._TestingPara
passedTests = []
nonFatalTests = []
failedTests = []
# Build a test sequence form each word
for index, val in enumerate(
self._TestingParaNlo.sentenceTokenList[1:]):
prevWord = self._TestingParaNlo.sentenceTokenList[index - 1][0]
prevWordToken = self._TestingParaNlo.sentenceTokenList[index -
1][1]
prevWordTokenNormal = self._TestingParaNlo.sentenceNormalised[index
- 1]
curWord = val[0]
curToken = val[1]
curNormal = self._TestingParaNlo.sentenceNormalised[index]
prediction = self.neuralNetworkV.getPredictedWord(
prevWordTokenNormal, curToken)
probList = self.neuralNetworkV.getPredictionProbability(
prevWordTokenNormal, curToken)
prob = 0
for val in probList[0]:
if (val > prob):
prob = val
if (str(curWord.lower()) == str(prediction).lower()):
passedTests.append("(" + str(prevWord) + ", " +
str(prevWordToken) + ") Target: " +
str(curWord) + " Pred: " +
str(prediction) + " " + str(prob * 100) +
"%")
else:
if (prob < 0.2):
failedTests.append("(" + str(prevWord) + ", " +
str(prevWordToken) +
") Target: " + str(curWord) +
" Pred: " + str(prediction) +
" " + str(prob * 100) + "%")
elif (prob > 0.6):
passedTests.append("(" + str(prevWord) + ", " +
str(prevWordToken) +
") Target: " + str(curWord) +
" Pred: " + str(prediction) +
" " + str(prob * 100) + "%")
else:
nonFatalTests.append("(" + str(prevWord) + ", " +
str(prevWordToken) +
") Target: " + str(curWord) +
" Pred: " + str(prediction) +
" " + str(prob * 100) + "%")
# print results
print("\n")
print("********** TestSentenceStructuring() **********")
print("\n")
ConsoleOutput.printUnderline("Failed Tests: (" +
str(len(failedTests)) + "/" +
str(len(testingPara)) + ")")
for val in failedTests:
ConsoleOutput.printRed(val)
print("\n")
ConsoleOutput.printUnderline("Non-Fatal failed Tests: (" +
str(len(nonFatalTests)) + "/" +
str(len(testingPara)) + ")")
for val in nonFatalTests:
ConsoleOutput.printYellow(val)
print("\n")
ConsoleOutput.printUnderline("Passed Tests: (" +
str(len(passedTests)) + "/" +
str(len(testingPara)) + ")")
for val in passedTests:
ConsoleOutput.printGreen(val)
print("\n")
ConsoleOutput.printYellow("Passed: " + str(len(passedTests)) +
" Non-Fatals: " + str(len(nonFatalTests)) +
" Fails: " + str(len(failedTests)))
print("\n")
def TestSentenceStructuring(self):
#testingPara = testingParaHarryPotter
testingPara = self._TestingPara
passedTests = []
nonFatalTests = []
failedTests = []
# used to predict accuracy of the network
acTestPred = []
acTestTrue = []
# Build a test sequence form each word
for index, val in enumerate(testingPara):
tmpTestSeq = []
target = None
# grab the next 3 words after
if (index < len(testingPara) - (self.VectorSizeSS + 1)):
for index2 in range(0, self.VectorSizeSS):
tmpTestSeq.append(testingPara[index + index2])
target = testingPara[index + self.VectorSizeSS]
# convert to natural language object
nloTester = NaturalLanguageObject(tmpTestSeq)
nloTarget = NaturalLanguageObject([target])
# get nerual network prediction
normalPred = self.neuralNetworkSS.getPrediction(
nloTester.sentenceNormalised)
prediction = str(nloTester.tokeniseNormals([normalPred]))
comp = str(nloTarget.sentenceTags)
cTrue = nloTarget.sentenceNormalised[0]
acTestTrue.append(cTrue * 100)
acTestPred.append(normalPred * 100)
#if first letters match, this means 'NN' will match with 'NNS'
if (prediction[2] == comp[2]):
#filter for probability
probList = self.neuralNetworkSS.getPredictionProbability(
nloTester.sentenceNormalised)
prob = 0
for val in probList[0]:
if (val > prob):
prob = val
passedTests.append(
str(nloTester.sentenceTokenList) + " Target: " +
str(nloTarget.sentenceTokenList) + " Prediction: " +
prediction + " " + str(prob * 100) + "%")
else:
probList = self.neuralNetworkSS.getPredictionProbability(
nloTester.sentenceNormalised)
prob = 0
for val in probList[0]:
if (val > prob):
prob = val
# if accuracy s less than 30% add to failed list
if (prob < 0.3):
failedTests.append(
str(nloTester.sentenceTokenList) + " Target: " +
str(nloTarget.sentenceTokenList) +
" Prediction: " + prediction + " " +
str(prob * 100) + "%")
else:
# if probability is more than 60% its probably passed
if (prob > 0.6):
passedTests.append(
str(nloTester.sentenceTokenList) +
" Target: " +
str(nloTarget.sentenceTokenList) +
" Prediction: " + prediction + " " +
str(prob * 100) + "%")
else:
nonFatalTests.append(
str(nloTester.sentenceTokenList) +
" Target: " +
str(nloTarget.sentenceTokenList) +
" Prediction: " + prediction + " " +
str(prob * 100) + "%")
# print results
print("\n")
print("********** TestSentenceStructuring() **********")
print("\n")
ConsoleOutput.printUnderline("Failed Tests: (" +
str(len(failedTests)) + "/" +
str(len(testingPara)) + ")")
for val in failedTests:
ConsoleOutput.printRed(val)
print("\n")
ConsoleOutput.printUnderline("Non-Fatal failed Tests: (" +
str(len(nonFatalTests)) + "/" +
str(len(testingPara)) + ")")
for val in nonFatalTests:
ConsoleOutput.printYellow(val)
print("\n")
ConsoleOutput.printUnderline("Passed Tests: (" +
str(len(passedTests)) + "/" +
str(len(testingPara)) + ")")
for val in passedTests:
ConsoleOutput.printGreen(val)
print("\n")
nnAccuracy = accuracy_score(
np.array(acTestTrue).astype(int),
np.array(acTestPred).astype(int))
ConsoleOutput.printYellow("Passed: " + str(len(passedTests)) +
" Non-Fatals: " + str(len(nonFatalTests)) +
" Fails: " + str(len(failedTests)))
ConsoleOutput.printYellow("NeuralNetork accuracy: " +
str(round(nnAccuracy * 100, 1)) + "%")
print("\n")
def TestSentenceStructuring(self):
#testingPara = testingParaHarryPotter
testingPara = self._TestingPara
passedTests = []
nonFatalTests = []
failedTests = []
# used to predict accuracy of the network
acTestPred = []
acTestTrue = []
# Build a test sequence form each word
for index, val in enumerate(testingPara):
tmpTestSeq = []
target = None
# grab the next 3 words after
if(index < len(testingPara)-(self.VectorSizeSS+1)):
for index2 in range(0, self.VectorSizeSS):
tmpTestSeq.append(testingPara[index+index2])
target = testingPara[index+self.VectorSizeSS]
# convert to natural language object
nloTester = NaturalLanguageObject(tmpTestSeq)
nloTarget = NaturalLanguageObject([target])
# get nerual network prediction
normalPred = self.neuralNetworkSS.getPrediction(nloTester.sentenceNormalised)
prediction = str(nloTester.tokeniseNormals([normalPred]))
comp = str(nloTarget.sentenceTags)
cTrue = nloTarget.sentenceNormalised[0]
acTestTrue.append(cTrue*100)
acTestPred.append(normalPred*100)
#if first letters match, this means 'NN' will match with 'NNS'
if(prediction[2] == comp[2]):
#filter for probability
probList = self.neuralNetworkSS.getPredictionProbability(nloTester.sentenceNormalised)
prob = 0
for val in probList[0]:
if(val > prob):
prob = val
passedTests.append(str(nloTester.sentenceTokenList) + " Target: " + str(nloTarget.sentenceTokenList) + " Prediction: "
+ prediction + " " +str(prob*100) + "%")
else:
probList = self.neuralNetworkSS.getPredictionProbability(nloTester.sentenceNormalised)
prob = 0
for val in probList[0]:
if(val > prob):
prob = val
# if accuracy s less than 30% add to failed list
if(prob < 0.3):
failedTests.append(str(nloTester.sentenceTokenList) + " Target: " + str(nloTarget.sentenceTokenList) + " Prediction: "
+ prediction + " " +str(prob*100) + "%")
else:
# if probability is more than 60% its probably passed
if(prob > 0.6):
passedTests.append(str(nloTester.sentenceTokenList) + " Target: " + str(nloTarget.sentenceTokenList) + " Prediction: "
+ prediction + " " +str(prob*100) + "%")
else:
nonFatalTests.append(str(nloTester.sentenceTokenList) + " Target: " + str(nloTarget.sentenceTokenList) + " Prediction: "
+ prediction + " " +str(prob*100) + "%")
# print results
print("\n")
print("********** TestSentenceStructuring() **********")
print("\n")
ConsoleOutput.printUnderline("Failed Tests: (" + str(len(failedTests)) + "/" + str(len(testingPara)) + ")")
for val in failedTests:
ConsoleOutput.printRed(val)
print("\n")
ConsoleOutput.printUnderline("Non-Fatal failed Tests: (" + str(len(nonFatalTests)) + "/" + str(len(testingPara)) + ")")
for val in nonFatalTests:
ConsoleOutput.printYellow(val)
print("\n")
ConsoleOutput.printUnderline("Passed Tests: (" + str(len(passedTests)) + "/" + str(len(testingPara)) + ")")
for val in passedTests:
ConsoleOutput.printGreen(val)
print("\n")
nnAccuracy = accuracy_score(np.array(acTestTrue).astype(int), np.array(acTestPred).astype(int))
ConsoleOutput.printYellow("Passed: " + str(len(passedTests)) + " Non-Fatals: " + str(len(nonFatalTests)) + " Fails: " + str(len(failedTests)))
ConsoleOutput.printYellow("NeuralNetork accuracy: " + str(round(nnAccuracy*100,1)) + "%")
print("\n")
