apriori-python

This is a simple implementation of Apriori Algorithm in Python Jupyter. It takes in a csv file with a list of transactions, and results out the association rules. The values for minimum_support and minimum_confidence need to be specified in the notebook.

Dependencies

• Python 3.9.0
• Jupyter

Understanding the implementation

Processing the input

1 We expect an input file to be a csv of the following format:

item1, item2, item3, ... so on
 , t, ...
t, t, t,...
t, t, ...
... so on...

2 We input the file using dataframes from pandas library:

df = pd.read_csv("myDataFile.csv", low_memory=False)

3 Next, we extract the header from the file and assign an index to each item. * We first read all the column headers into item_list. * Then, we index these headers. This is done by making a dictionary such that item_dict[item_name] = index_value. * These index values are serial integers for every item in item_list.

item_list = list(df.columns)
item_dict = dict()

for i, item in enumerate(item_list):
    item_dict[item] = i + 1

4. Now, we need to extract individual transactions from the data file.
   • First we create an empty list transactions.
   • Then, we iterate through the rows in the dataframe df.
   • We create a local variable transaction as an empty set.
   • Now we iterate through all the possible column items from item_dict, and check if the value of that column in this row is t, i.e., true. If it is, then we add the assigned index value of this item to this transaction set.
   • Finally, we append this transaction to the list of all transactions.
   • Thus, transactions is a list, where each transaction is a set of item index values.

transactions = list()

for i, row in df.iterrows():
    transaction = set()

    for item in item_dict:
        if row[item] == 't':
            transaction.add(item_dict[item])
    transactions.append(transaction)

---

Implement Apriori Principle to determine frequent datasets

1. We first define some Utility Functions first.

2. get_support(transactions, item_set): This function calculates the support value for the given item_set from the provided list of transactions.

   • It initialises a local variable match_count to store the number of transactions where the given item_set is found.
   • It then iterates through the the list of transactions
   • For each transaction, it is checked whether the given item_set is a subset of the transaction or not. If it is, match_count is incremented.
   • Finally support value calculated by dividing the match_count by total number of transactions is returned

def get_support(transactions, item_set):
    match_count = 0
    for transaction in transactions:
        if item_set.issubset(transaction):
            match_count += 1

    return float(match_count/len(transactions))

3. self_join(frequent_item_sets_per_level, level): This function performs self join in the given list of frequent itemsets of previous level, and generates the candidate itemsets for the current level.
   • It takes 2 inputs: frequent_item_sets_per_level is a map of level to the list of itemsets found to be frequent for that level. Second argument is the current level number.
   • It first initialises the current_level_candidates as an empty list, and last_level_items as the list of frequent itemsets from the previous level.
   • If there are no frequent itemsets from the previous level, it returns an empty list for current_level_candidates. Otherwise, it iterates through each itemset in last_level_items starting from 0 for index i, and for each itemset in last_level_items starting from 1 for index j.
   • It performs the union of itemsets at indices i and j.
   • If this union_set is not already present in current_level_candidates and the number of elements in the union_set is equal to the level number, then this union_set is appended into current_level_candidates.
   • We have the check for the number of elements in union_set to ensure that the current_level_candidates contain only the sets of fixed length. This is a requirement for Apriori Algorithm
   • Finally, current_level_candidates is returned.

def self_join(frequent_item_sets_per_level, level):
    current_level_candidates = list()
    last_level_items = frequent_item_sets_per_level[level - 1]

    if len(last_level_items) == 0:
        return current_level_candidates

    for i in range(len(last_level_items)):
        for j in range(i+1, len(last_level_items)):
            itemset_i = last_level_items[i][0]
            itemset_j = last_level_items[j][0]
            union_set = itemset_i.union(itemset_j)

            if union_set not in current_level_candidates and len(union_set) == level:
                current_level_candidates.append(union_set)

    return current_level_candidates

4. get_single_drop_subsets(item_set): This function returns the subsets of the given item_set with one item less.
   • We first initialize the variable single_drop_subsets as an empty list.
   • Next, for each item in item_set, we create a temporary set temp as a copy of the item_set given.
   • We then remove this item from the temp set. It results in a subset of item_set without the item, i.e., a subset of length one less than the length of the item_set
   • We then append this temp set to the single_drop_subsets
   • Finally, we return the list single_drop_subsets.

def get_single_drop_subsets(item_set):
    single_drop_subsets = list()
    for item in item_set:
        temp = item_set.copy()
        temp.remove(item)
        single_drop_subsets.append(temp)

    return single_drop_subsets

5. is_valid_set(item_set, prev_level_sets): This checks if the given item_set is valid, i.e., has all its subsets with support value greater than the minimum support value. It relies on the fact that prev_level_sets contains only those item_sets which are frequent, i.e., have support value greater than the minimum support value.
   • It first generates all the subsets of the given item_set with length one less than the length of the original item_set. This is done using the above described function get_single_drop_subsets(). These subsets are stored in single_drop_subsets variable
   • It then iterates through the single_drop_subsets list.
   • For each single_drop_subset, it checks if it was present in the prev_level_sets. If it wasn’t it means the given item_set is a superset of a non-frequent item_set. Thus, it returns False
   • If all the single_drop_subsets are frequent itemsets, and are present in the prev_level_sets, it returns True

def is_valid_set(item_set, prev_level_sets):
    single_drop_subsets = get_single_drop_subsets(item_set)

    for single_drop_set in single_drop_subsets:
        if single_drop_set not in prev_level_sets:
            return False
    return True

6. pruning(frequent_item_sets_per_level, level, candidate_set): This function performs the pruning step of the Apriori Algorithm. It takes a list candidate_set of all the candidate itemsets for the current level, and for each candidate itemset checks if all its subsets are frequent itemsets. If not, it prunes it, If yes, it adds it to the list of post_pruning_set.
   • It first initialises an empty list variable post_pruning_set. This is to store the list of frequent itemsets for the current level after performing pruning operation on the given list of candidate sets.
   • If there are no candidate_set, it returns an empty list post_pruning_set.
   • Otherwise, it first creates a list of frequent itemsets from the previous level and stores it in prev_level_sets.
   • Then, it iterates over each item_set in candidate_set list.
   • For each item_set, it checks whether it is a valid itemset or not. This is done using the above described function is_valid_set(). This function uses the fact that all the subsets of the given item_set (formed by removing one item) need to be frequent itemsets for this item_set to be valid.
   • If this item_set is valid, it is appended to the list of post_pruning_set.
   • Finally post_pruning_set is returned.

def pruning(frequent_item_sets_per_level, level, candidate_set):
    post_pruning_set = list()
    if len(candidate_set) == 0:
        return post_pruning_set

    prev_level_sets = list()
    for item_set, _ in frequent_item_sets_per_level[level - 1]:
        prev_level_sets.append(item_set)

    for item_set in candidate_set:
        if is_valid_set(item_set, prev_level_sets):
            post_pruning_set.append(item_set)

    return post_pruning_set

7. apriori(min_support): This is the main function which uses all the above described Utility functions to implement the Apriori Algorithm and generate the list of frequent itemsets for each level for the provided transactions and min_support value.
   • It first creates a default empty dictionary frequent_item_sets_per_level, which maps level numbers to the list of frequent itemsets for that level.
   • Next, it handles the first level itemsets. It means all the itemsets with only one item. To generate such itemsets, we iterate through the list of all items item_list. We calculate the support value of each item using the utility function get_support(). If this support value is greater than or equal to the provided min_support value, this item_set is added to the list of frequent itemsets for this level.
   • One thing to note here is that every itemset is stored as a pair of 2 values:
     • The itemset
     • The support value calculated for this itemset
   • Now, we handle the levels greater than 1
   • For each level greater than 1, we first generate the current_level_candidates itemsets by performing self_join() on the frequent itemsets of the previous level.
   • Next, we perform the pruning operation on these current_level_candidates using the pruning() utility function described above, and obtain the results in post_pruning_candidates
   • Now, if there is no itemset left after pruning, we break the loop. It means there is no point in processing for further levels.
   • Otherwise, for each item_set in post_pruning_candidates, we calculate the support value using the get_support() utility function.
   • If this support value is greater than or equal to the given min_support, we append this item_set into the list of frequent itemsets for this level.
   • Note that this append operation also happens in pair format as described above.
   • Finally, we return the dictionary frequent_item_sets_per_level.

from collections import defaultdict

def apriori(min_support):
    frequent_item_sets_per_level = defaultdict(list)
    print("level : 1", end = " ")

    for item in range(1, len(item_list) + 1):
        support = get_support(transactions, {item})
        if support >= min_support:
            frequent_item_sets_per_level[1].append(({item}, support))

    for level in range(2, len(item_list) + 1):
        print(level, end = " ")
        current_level_candidates = self_join(frequent_item_sets_per_level, level)

        post_pruning_candidates = pruning(frequent_item_sets_per_level, level, current_level_candidates)
        if len(post_pruning_candidates) == 0:
            break

        for item_set in post_pruning_candidates:
            support = get_support(transactions, item_set)
            if support >= min_support:
                frequent_item_sets_per_level[level].append((item_set, support))

    return frequent_item_sets_per_level

8. We specify the minimum support value for the given data here in variable min_support and invoke the apriori() function to generate the frequent_item_sets_per_level.

min_support = 0.05
frequent_item_sets_per_level = apriori(min_support)

---

Implementation of the non-monotonicity property in the determination of the association rules

1. Below code produces a dictionary called item_support_dict from frequent_item_sets_per_level that maps items to their support values.
   • First, a dictionary called item_support_dict is created to store key value pairs of items and their support values, and an empty list called item_list is created to store the name of items corresponding to item_dict values retrieved from frequent_item_sets_per_level.
   • Keys and values are retrieved from the item_dict and put inside a list for the later use.
   • For each level in frequent_item_sets_per_level, for each item-support pair, name of the item retrieved from the key_list that corresponds to the number in set_support_pair, and names are added to the item_list.
   • Items names and their support values are mapped in the item_support_dict as a frozenset-float number pair.

item_support_dict = dict()
item_list = list()

key_list = list(item_dict.keys())
val_list = list(item_dict.values())

for level in frequent_item_sets_per_level:
    for set_support_pair in frequent_item_sets_per_level[level]:
        for i in set_support_pair[0]:
            item_list.append(key_list[val_list.index(i)])
        item_support_dict[frozenset(item_list)] = set_support_pair[1]
        item_list = list()

2. find_subset(item, item_length): This function takes each item from the item_support_dict and its length item_length as parameter, and returns all possible combinations of elements inside the items.
   • It first creates an empty array called combs to store a list of combinations.
   • It appends a list of all possible combinations of items to the combs array.
   • To reach the combinations from an array directly, for comb array in combs array, and for each elt in comb array, each element appended to the subsets array.

def find_subset(item, item_length):
    combs = []
    for i in range(1, item_length + 1):
        combs.append(list(combinations(item, i)))

    subsets = []
    for comb in combs:
        for elt in comb:
            subsets.append(elt)

    return subsets

3. association_rules(min_confidence, support_dict): This function generates the association rules in accordance with the given minimum confidence value and the provided dictionary of itemsets against their support values. It takes min_confidence and support_dict as a parameter, and returns rules as a list.
   • For itemsets of more than one element, it first finds all their subsets calling the find_subset(item, item_length) function.
   • For every subset A, it calculates the set B = itemset-A.
   • If B is not empty, the confidence of B is calculated.
   • If this value is more than minimum confidence value, the rule A->B is added to the list rules with the corresponding confidence value of B.

def association_rules(min_confidence, support_dict):
    rules = list()
    for item, support in support_dict.items():
        item_length = len(item)

        if item_length > 1:
            subsets = find_subset(item, item_length)

            for A in subsets:
                B = item.difference(A)

                if B:
                    A = frozenset(A)

                    AB = A | B

                    confidence = support_dict[AB] / support_dict[A]
                    if confidence >= min_confidence:
                        rules.append((A, B, confidence))

    return rules

---

Output Processing

1. Output of the association_rules(min_confidence, support_dict) function is calculated for given min_confidence=0.6 below.

association_rules = association_rules(min_confidence = 0.6, support_dict = item_support_dict)

2. Number of rules and association_rules are printed. Rules are printed in the form of A -> B <confidence: ... >, where A and B can be a comma separated list, if they consist of more than one item.

print("Number of rules: ", len(association_rules), "\n")

for rule in association_rules:
    print('{0} -> {1} <confidence: {2}>'.format(set(rule[0]), set(rule[1]), rule[2]))