log_loss(test_y, predict_y, labels=clf.classes_, eps=1e-15)) clf = KNeighborsClassifier(n_neighbors=alpha[best_alpha]) predict_and_plot_confusion_matrix(train_x_responseCoding, train_y, cv_x_responseCoding, cv_y, clf) clf = KNeighborsClassifier(n_neighbors=alpha[best_alpha]) clf.fit(train_x_responseCoding, train_y) sig_clf = CalibratedClassifierCV(clf, method='sigmoid') sig_clf.fit(train_x_responseCoding, train_y) test_point_index = 1 predicted_cls = sig_clf.predict(test_x_responseCoding[0].reshape(1, -1)) print(" Actual class :", predicted_cls[0]) print("Predicted class :", test_y[test_point_index]) neighbors = clf.kneighbors(test_x_responseCoding[test_point_index].reshape( 1, -1)) print("the ", alpha[best_alpha], "nearest neighbors of test points belong to class", neighbors) #print("Frequency of nearesr point :",Counter(train_y(neighbors[1][0]))) clf = KNeighborsClassifier(n_neighbors=alpha[best_alpha]) clf.fit(train_x_responseCoding, train_y) sig_clf = CalibratedClassifierCV(clf, method='sigmoid') sig_clf.fit(train_x_responseCoding, train_y) test_point_index = 100 predicted_cls = sig_clf.predict(test_x_responseCoding[0].reshape(1, -1)) print(" Actual class :", predicted_cls[0]) print("Predicted class :", test_y[test_point_index]) neighbors = clf.kneighbors(test_x_responseCoding[test_point_index].reshape( 1, -1))
clf = KNeighborsClassifier(n_neighbors=alpha[best_alpha]) predict_and_plot_confusion_matrix(train_x_responseCoding, train_y, cv_x_responseCoding, cv_y, clf) # Lets look at few test points clf = KNeighborsClassifier(n_neighbors=alpha[best_alpha]) clf.fit(train_x_responseCoding, train_y) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(train_x_responseCoding, train_y) test_point_index = 1 predicted_cls = sig_clf.predict(test_x_responseCoding[0].reshape(1, -1)) print("Predicted Class :", predicted_cls[0]) print("Actual Class :", test_y[test_point_index]) neighbors = clf.kneighbors( test_x_responseCoding[test_point_index].reshape(1, -1), alpha[best_alpha]) print("The ", alpha[best_alpha], " nearest neighbours of the test points belongs to classes", train_y[neighbors[1][0]]) print("Fequency of nearest points :", Counter(train_y[neighbors[1][0]])) #logistic Regression alpha = [10**x for x in range(-6, 3)] cv_log_error_array = [] for i in alpha: print("for alpha =", i) clf = SGDClassifier(class_weight='balanced', alpha=i, penalty='l2', loss='log',