def getTimeOfEvaluation(self, X, Y):
plotX = []
plotY = []
i = 0
while (i < 8):
plotX.append(i)
startTime = time.time()
print("Start training using logistic regression")
model = MLModel.LogisticRegressionModel(X, Y, X, Y)
model.fit(0.1**(7 - i))
timeUsed = time.time() - startTime
plotY.append(timeUsed)
i = i + 1
plt.plot(plotX, plotY, c='r', label='logistic regression')
plt.xlabel("i such that learningRate = 0.1^(7-i)")
plt.ylabel("RunningTime(second)")
plt.title("Performance of LogisticRegression based on learningRate")
plt.show()
def validate(self, X, Y, k, isLogisticRegression, learningRate):
(row, col) = X.shape
numOfSection = int(row / k)
totalAcc = 0.0
for a in range(0, k):
i = a * numOfSection
Xbefore = []
Ybefore = []
if i == 0:
Xbefore = X[0:0, 0:0]
Ybefore = Y[0:0]
else:
Xbefore = X[0:i - 1, 0:col]
Ybefore = Y[0:i - 1]
subX = X[i:i + numOfSection - 1, 0:col]
subY = Y[i:i + numOfSection - 1]
Xafter = X[i + numOfSection:row, 0:col]
Yafter = Y[i + numOfSection:row]
XConc = []
YConc = []
if i == 0:
Xconc = Xafter
Yconc = Yafter
else:
Xconc = np.concatenate((Xbefore, Xafter))
Yconc = np.concatenate((Ybefore, Yafter))
#Use to test the accuracy value from sci-kit
model = None
if (isLogisticRegression):
print("Start training using logistic regression")
model = MLModel.LogisticRegressionModel(
Xconc, Yconc, subX, subY)
model.fit(learningRate)
else:
print("Start training using gaussian naive bayes")
model = MLModel.GaussianNaiveBayes(Xconc, Yconc, subX, subY)
model.fit()
targetY = model.predict(subX)
acc = model.evaluate_acc(subY, targetY)
#print("Accuracy is " + str(acc))
totalAcc = totalAcc + acc
return (totalAcc / float(k))