import numpy as np
import matplotlib.pyplot as plt
from sklearn import naive_bayes
from sklearn import metrics
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris = load_iris()

is_versicolor = iris.target == 1

(tain_features, test_features, train_target, test_target) = train_test_split(iris.data, is_versicolor, test_size=0.33, random_state=21)

model = naive_bayes.GaussianNB()
model.fit(tain_features, train_target)
predictions = model.predict_proba(test_features)[:,1] # [:,1]=="True"
print(predictions)

fpr, tpr, thresh = metrics.roc_curve(test_target, predictions)
auc = metrics.auc(fpr, tpr)
print("FPR : {}".format(fpr),
  "TPR : {}".format(tpr), sep='\n')
print(thresh)

# glavni graf
fig, ax = plt.subplots(figsize=(8, 4))
ax.plot(fpr, tpr, 'o--')
ax.set_title("1-Class Iris ROC Curve\nAUC:{:.3f}".format(auc))
ax.set_xlabel("FPR")
ax.set_ylabel("TPR")

# oznacavanje tocaka
investigate = np.array([1, 3, 5])
for idx in investigate:
  th, f, t = thresh[idx], fpr[idx], tpr[idx]
  ax.annotate('thresh = {:.3f}'.format(th),
    xy=(f+.01, t-.01), xytext=(f+.1, t),
    arrowprops = {'arrowstyle':'->'})