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()]
plt.show()
'''
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)
