eriklindernoren--ml-from-scratch
eriklindernoren
35 行
1.2 KiB
Python
35 行
1.2 KiB
Python
from __future__ import division, print_function
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import numpy as np
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from mlfromscratch.unsupervised_learning import Apriori
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def main():
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# Demo transaction set
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# Example 2: https://en.wikipedia.org/wiki/Apriori_algorithm
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transactions = np.array([[1, 2, 3, 4], [1, 2, 4], [1, 2], [2, 3, 4], [2, 3], [3, 4], [2, 4]])
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print ("+-------------+")
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print ("| Apriori |")
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print ("+-------------+")
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min_sup = 0.25
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min_conf = 0.8
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print ("Minimum Support: %.2f" % (min_sup))
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print ("Minimum Confidence: %s" % (min_conf))
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print ("Transactions:")
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for transaction in transactions:
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print ("\t%s" % transaction)
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apriori = Apriori(min_sup=min_sup, min_conf=min_conf)
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# Get and print the frequent itemsets
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frequent_itemsets = apriori.find_frequent_itemsets(transactions)
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print ("Frequent Itemsets:\n\t%s" % frequent_itemsets)
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# Get and print the rules
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rules = apriori.generate_rules(transactions)
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print ("Rules:")
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for rule in rules:
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print ("\t%s -> %s (support: %.2f, confidence: %s)" % (rule.antecedent, rule.concequent, rule.support, rule.confidence,))
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if __name__ == "__main__":
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main() |