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print("Hello")

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# set ads
num_ads = 3
ads = np.asarray(["ad_{}".format(i) for i in range(num_ads)])

# assign random priors to contexts
ad_interaction_priors = np.asarray([0.1, 0.3, 0.6])
user_context_priors = {context:np.random.permutation(ad_interaction_priors) for context in user_contexts}

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# calculate precision-recall AUC
auc_prc = auc(recall, precision)
print(auc_prc)

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precision, recall, thresholds = precision_recall_curve(y_test, y_pred_proba)

plt.figure(figsize=(10,8))
plt.plot([0, 1], [0.5, 0.5],'k--')
plt.plot(recall, precision, label='Knn')
plt.xlabel('recall')
plt.ylabel('precision')
plt.title('Knn(n_neighbors=5) PRC curve')
plt.show()

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roc_auc_score(y_test, y_pred_proba)

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plt.figure(figsize=(10,8))
plt.plot([0,1],[0,1],'k--')
plt.plot(fpr,tpr, label='Knn')
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('Knn(n_neighbors=5) ROC curve')
plt.show()

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y_pred_proba = knn.predict_proba(X_test)[:,1]
fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)

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print(classification_report(y_test,y_pred))

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y_pred = knn.predict(X_test)
confusion_matrix(y_test,y_pred)
pd.crosstab(y_test, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)

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#Setup a knn classifier with k neighbors
knn = KNeighborsClassifier(8)

knn.fit(X_train,y_train)
knn.score(X_test,y_test)