def Eval(self, test):
Y_pred = self.PredictLabel(test.X,0,True)
recall_pos = []
recall_neg = []
precision_pos = []
precision_neg =[]
ev = Eval(Y_pred, test.Y)
accy = ev.Accuracy()
#print(ev.EvalRecall())
#print(ev.EvalPrecision())
probThresh = [0.2,0.4,0.6,0.8]
for i in range(len(probThresh)):
pred = self.PredictLabel(test.X,probThresh[i],False)
ev = Eval(pred, test.Y)
#ev.EvalRecall()
#print('Threshold Value %f'%probThresh[i])
recallP,recallN=ev.EvalRecall()
recall_pos.append(recallP)
recall_neg.append(recallN)
precisionP,precisionN=ev.EvalPrecision()
precision_pos.append(precisionP)
precision_neg.append(precisionN)
plt.pause(0.1)
plt.xlabel('Recall')
plt.ylabel('Precision')
fig=plt.figure()
plt.title('For +1 Label')
plt.plot(recall_pos,precision_pos,'r')
fig.savefig('Pos_class.png')
plt.title('For -1 Label')
fig1=plt.figure(1)
plt.plot(recall_neg,precision_neg,'b')
fig1.savefig('Neg_class.png')
return accy
def Eval(self, test):
Y_pred = self.PredictLabel(test.X)
# Y_pred = self.PredictLabel(test,len(test))
ev = Eval(Y_pred, test.Y)
# ev = Eval(Y_pred.pred, test.Y)
return [ev.Accuracy(), ev.EvalPrecition(), ev.EvalRecall()]
'''
print("Log Probability of first 10 review")
nb = NaiveBayes(traindata, float(sys.argv[2]))
for i in range(10):
res = nb.PredictProb(testdata, i)
print(i, res)
print("Predict Label with Threshold")
precition = []
recall = []
for i in np.arange(0.1, 1.0, 0.1):
pred = nb.PredictLabelTh(testdata, i)
ev = Eval(pred, testdata.Y)
precition.append(ev.EvalPrecition())
recall.append(ev.EvalRecall())
# ev = Eval(Y_pred.pred, test.Y)
print('Threshold:', i, ' Accuracy: ', ev.Accuracy(), ' Precition: ',
ev.EvalPrecition(), ' Recall: ', ev.EvalRecall())
'''
# Create some mock data
t = recall
data1 = precition
data2 = np.arange(0.05, 1.0, 0.05)
fig, ax1 = plt.subplots()
color = 'tab:blue'
ax1.set_xlabel('Recall')
ax1.set_ylabel('Precition', color=color)
ax1.plot(t, data1, color=color)
ax1.tick_params(axis='y', labelcolor=color)
ax2 = ax1.twinx()
