Top category missing from ordinal classifier

Question

I am trying to implement an ordinal classifier based on the sources below:

• original source: Simple Trick to Train an Ordinal Regression with any Classifier
• updated coding: How to use ordinal classifier?

In my training dataset, I have 5 categories (usefulness level 1 - level 5). However, I only end up with level 1- level 4 usefulness in the prediction for the test dataset using the code above (the answer in How to use ordinal classifier?). I have enough samples in both training (n=1,000) and test (n=15,000) datasets.

from sklearn.base import clone
import numpy as np

# Source:
# 1. https://stackoverflow.com/questions/66486947/how-to-use-ordinal-classifier
# 2. https://towardsdatascience.com/simple-trick-to-train-an-ordinal-regression-with-any-classifier-6911183d2a3c


class OrdinalClassifier():

    def __init__(self, clf):
    self.clf = clf
    self.clfs = {}

def fit(self, X, y):
    self.unique_class = np.sort(np.unique(y))
    if self.unique_class.shape[0] > 2:
        for i in range(self.unique_class.shape[0] - 1):
            # for each k - 1 ordinal value we fit a binary classification problem
            binary_y = (y > self.unique_class[i]).astype(np.uint8)
            clf = clone(self.clf)
            clf.fit(X, binary_y)
            self.clfs[i] = clf

def predict_proba(self, X):
    clfs_predict = {k: v.predict_proba(X) for k, v in self.clfs.items()}
    predicted = []
    for i, y in enumerate(self.unique_class):
        if i == 0:
            # V1 = 1 - Pr(y > V1)
            predicted.append(1 - clfs_predict[i][:, 1])
        elif y in clfs_predict:
            # Vi = Pr(y > Vi-1) - Pr(y > Vi)
            predicted.append(clfs_predict[i - 1][:, 1] - clfs_predict[i][:, 1])
        else:
            # Vk = Pr(y > Vk-1)
            predicted.append(clfs_predict[i - 1][:, 1])
    return np.vstack(predicted).T

def predict(self, X):
    return self.unique_class[np.argmax(self.predict_proba(X), axis=1)]

knn = KNeighborsClassifier()
oc = OrdinalClassifier(knn)
oc.fit(X_train, y_train)
oc.predict(X_test)

Stats for each class in the training dataset and the three prediction models