human
Solve the following python programming problem given the following code:
# 1. 
# a. select neighbors whose similarity correlation is greater than the threshold of 0.5
# b. remove user pairs that do not share a minimum of 5 reviews

# output: number of user pairs that passes minPearson = 1692207
#         number of user pairs that passes both minPearson and minSimilarReviews = 533407

minPearson = 0.5
minSimilarReviews = 5

userPairPassThreshold = userPairSimilarities.filter(
    lambda (userPair, (pearson_correlation, num_rating_pairs)): 
        pearson_correlation > minPearson and 
        num_rating_pairs >= minSimilarReviews
    )
print userPairPassThreshold.count()
userPairPassThreshold.take(5)

# 2. select top n neighbors for each test user  

from pyspark.rdd import RDD

import heapq

def takeOrderedByKey(self, topN, sortValueFn=None, ascending=False):

    def base(a):
        return [a]

    def combiner(agg, a):
        agg.append(a)
        return getTopN(agg)

    def merger(x, y):
        agg = x + y
        return getTopN(agg)

    def getTopN(agg):
        if ascending == True:
            return heapq.nsmallest(topN, agg, sortValueFn)
        else:
            return heapq.nlargest(topN, agg, sortValueFn)              

    return self.combineByKey(base, combiner, merger)
 
# add takeOrderedByKey() function to RDD class
RDD.takeOrderedByKey = takeOrderedByKey

# convert 
# [( (test_user_id, train_user_id), (pearson_correlation, num_rating_pairs) )]
# to
# [( test_user_id, [(test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)] )]
# so that you can sort by test_user_id after sorting the highest pearson correlation per test_user_id
testU_testUtrainU_sim = userPairPassThreshold.map(
    lambda ((test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)): 
        (test_user_id, ((test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)))
)
print testU_testUtrainU_sim.count()
testU_testUtrainU_sim.take(5)

# for each test user, take the top N neighbors and ordering with the highest pearson correlation first
# [( test_user_id,...
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python program to print userPairPassThreshold.

find nearest neighbors
1. select neighbors whose similarity correlation is greater than the threshold of 0.5
2. select top n neighbors with the highest correlation