Scikit-learn predict_proba gives wrong answers

Question

This is a follow-up question from How to know what classes are represented in return array from predict_proba in Scikit-learn

In that question, I quoted the followin

Answer 1

Food for thought here. I think i actually got predict_proba to work as is. Please see code below...

# Test data
TX = [[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15], [16,17,18], [19,20,21], [22,23,24]]
TY = ['apple', 'orange', 'grape', 'kiwi', 'mango','peach','banana','pear']

VX2 = [[16,17,18], [19,20,21], [22,23,24], [13,14,15], [10,11,12], [7,8,9], [4,5,6], [1,2,3]]
VY2 = ['peach','banana','pear','mango', 'kiwi', 'grape', 'orange','apple']

VX2_df = pd.DataFrame(data=VX2) # convert to dataframe
VX2_df = VX2_df.rename(index=float, columns={0: "N0", 1: "N1", 2: "N2"})
VY2_df = pd.DataFrame(data=VY2) # convert to dataframe
VY2_df = VY2_df.rename(index=float, columns={0: "label"})

# NEW - in testing
def train_model(classifier, feature_vector_train, label, feature_vector_valid, valid_y, valid_x, is_neural_net=False):

    # fit the training dataset on the classifier
    classifier.fit(feature_vector_train, label)

    # predict the top n labels on validation dataset
    n = 5
    #classifier.probability = True
    probas = classifier.predict_proba(feature_vector_valid)
    predictions = classifier.predict(feature_vector_valid)

    #Identify the indexes of the top predictions
    #top_n_predictions = np.argsort(probas)[:,:-n-1:-1]
    top_n_predictions = np.argsort(probas, axis = 1)[:,-n:]

    #then find the associated SOC code for each prediction
    top_socs = classifier.classes_[top_n_predictions]

    #cast to a new dataframe
    top_n_df = pd.DataFrame(data=top_socs)

    #merge it up with the validation labels and descriptions
    results = pd.merge(valid_y, valid_x, left_index=True, right_index=True)
    results = pd.merge(results, top_n_df, left_index=True, right_index=True)

    conditions = [
        (results['label'] == results[0]),
        (results['label'] == results[1]),
        (results['label'] == results[2]),
        (results['label'] == results[3]),
        (results['label'] == results[4])]
    choices = [1, 1, 1, 1, 1]
    results['Successes'] = np.select(conditions, choices, default=0)

    print("Top 5 Accuracy Rate = ", sum(results['Successes'])/results.shape[0])
    print("Top 1 Accuracy Rate = ", metrics.accuracy_score(predictions, valid_y))

train_model(naive_bayes.MultinomialNB(), TX, TY, VX2, VY2_df, VX2_df)

Output: Top 5 Accuracy Rate = 1.0 Top 1 Accuracy Rate = 1.0

Couldn't get it to work for my own data though :(