# Source code for efficient_apriori.rules

```
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Implementations of algorithms related to association rules.
"""
import typing
import numbers
import itertools
from efficient_apriori.itemsets import apriori_gen
[docs]
class Rule:
"""
A class for a rule.
"""
# Number of decimals used for printing
_decimals = 3
def __init__(
self,
lhs: tuple,
rhs: tuple,
count_full: int = 0,
count_lhs: int = 0,
count_rhs: int = 0,
num_transactions: int = 0,
):
"""
Initialize a new rule. This call is a thin wrapper around some data.
Parameters
----------
lhs : tuple
The left hand side (antecedent) of the rule. Each item in the tuple
must be hashable, e.g. a string or an integer.
rhs : tuple
The right hand side (consequent) of the rule.
count_full : int
The count of the union of the lhs and rhs in the dataset.
count_lhs : int
The count of the lhs in the dataset.
count_rhs : int
The count of the rhs in the dataset.
num_transactions : int
The number of transactions in the dataset.
Examples
--------
>>> r = Rule(('a', 'b'), ('c',), 50, 100, 150, 200)
>>> r.confidence # Probability of 'c', given 'a' and 'b'
0.5
>>> r.support # Probability of ('a', 'b', 'c') in the data
0.25
>>> # Ratio of observed over expected support if lhs, rhs = independent
>>> r.lift == 2 / 3
True
>>> print(r)
{a, b} -> {c} (conf: 0.500, supp: 0.250, lift: 0.667, conv: 0.500)
>>> r
{a, b} -> {c}
"""
self.lhs = lhs # antecedent
self.rhs = rhs # consequent
self.count_full = count_full
self.count_lhs = count_lhs
self.count_rhs = count_rhs
self.num_transactions = num_transactions
@property
def confidence(self):
"""
The confidence of a rule is the probability of the rhs given the lhs.
If X -> Y, then the confidence is P(Y|X).
"""
try:
return self.count_full / self.count_lhs
except ZeroDivisionError:
return None
except AttributeError:
return None
@property
def support(self):
"""
The support of a rule is the frequency of which the lhs and rhs appear
together in the dataset. If X -> Y, then the support is P(Y and X).
"""
try:
return self.count_full / self.num_transactions
except ZeroDivisionError:
return None
except AttributeError:
return None
@property
def lift(self):
"""
The lift of a rule is the ratio of the observed support to the expected
support if the lhs and rhs were independent.If X -> Y, then the lift is
given by the fraction P(X and Y) / (P(X) * P(Y)).
"""
try:
observed_support = self.count_full / self.num_transactions
prod_counts = self.count_lhs * self.count_rhs
expected_support = prod_counts / self.num_transactions**2
return observed_support / expected_support
except ZeroDivisionError:
return None
except AttributeError:
return None
@property
def conviction(self):
"""
The conviction of a rule X -> Y is the ratio P(not Y) / P(not Y | X).
It's the proportion of how often Y does not appear in the data to how
often Y does not appear in the data, given X. If the ratio is large,
then the confidence is large and Y appears often.
"""
try:
eps = 10e-10 # Avoid zero division
prob_not_rhs = 1 - self.count_rhs / self.num_transactions
prob_not_rhs_given_lhs = 1 - self.confidence
return prob_not_rhs / (prob_not_rhs_given_lhs + eps)
except ZeroDivisionError:
return None
except AttributeError:
return None
@property
def rpf(self):
"""
The RPF (Rule Power Factor) is the confidence times the support.
"""
try:
return self.confidence * self.support
except ZeroDivisionError:
return None
except AttributeError:
return None
@staticmethod
def _pf(s):
"""
Pretty formatting of an iterable.
"""
return "{" + ", ".join(str(k) for k in s) + "}"
def __repr__(self):
"""
Representation of a rule.
"""
return "{} -> {}".format(self._pf(self.lhs), self._pf(self.rhs))
def __str__(self):
"""
Printing of a rule.
"""
conf = "conf: {0:.3f}".format(self.confidence)
supp = "supp: {0:.3f}".format(self.support)
lift = "lift: {0:.3f}".format(self.lift)
conv = "conv: {0:.3f}".format(self.conviction)
return "{} -> {} ({}, {}, {}, {})".format(self._pf(self.lhs), self._pf(self.rhs), conf, supp, lift, conv)
def __eq__(self, other):
"""
Equality of two rules.
"""
return (set(self.lhs) == set(other.lhs)) and (set(self.rhs) == set(other.rhs))
def __hash__(self):
"""
Hashing a rule for efficient set and dict representation.
"""
return hash(frozenset(self.lhs + self.rhs))
def __len__(self):
"""
The length of a rule, defined as the number of items in the rule.
"""
return len(self.lhs + self.rhs)
def generate_rules_simple(
itemsets: typing.Dict[int, typing.Dict],
min_confidence: float,
num_transactions: int,
):
"""
DO NOT USE. This is a simple top-down algorithm for generating association
rules. It is included here for testing purposes, and because it is
mentioned in the 1994 paper by Agrawal et al. It is slow because it does
not enumerate the search space efficiently: it produces duplicates, and it
does not prune the search space efficiently.
Simple algorithm for generating association rules from itemsets.
"""
# Iterate over every size
for size in itemsets.keys():
# Do not consider itemsets of size 1
if size < 2:
continue
# This algorithm returns duplicates, so we keep track of items yielded
# in a set to avoid yielding duplicates
yielded: set = set()
yielded_add = yielded.add
# Iterate over every itemset of the prescribed size
for itemset in itemsets[size].keys():
# Generate rules
for result in _genrules(itemset, itemset, itemsets, min_confidence, num_transactions):
# If the rule has been yieded, keep going, else add and yield
if result in yielded:
continue
yielded_add(result)
yield result
def _genrules(l_k, a_m, itemsets, min_conf, num_transactions):
"""
DO NOT USE. This is the gen-rules algorithm from the 1994 paper by Agrawal
et al. It's a subroutine called by `generate_rules_simple`. However, the
algorithm `generate_rules_simple` should not be used.
The naive algorithm from the original paper.
Parameters
----------
l_k : tuple
The itemset containing all elements to be considered for a rule.
a_m : tuple
The itemset to take m-length combinations of, an move to the left of
l_k. The itemset a_m is a subset of l_k.
"""
def count(itemset):
"""
Helper function to retrieve the count of the itemset in the dataset.
"""
return itemsets[len(itemset)][itemset]
# Iterate over every k - 1 combination of a_m to produce
# rules of the form a -> (l - a)
for a_m in itertools.combinations(a_m, len(a_m) - 1):
# Compute the count of this rule, which is a_m -> (l_k - a_m)
confidence = count(l_k) / count(a_m)
# Keep going if the confidence level is too low
if confidence < min_conf:
continue
# Create the right hand set: rhs = (l_k - a_m) , and keep it sorted
rhs = set(l_k).difference(set(a_m))
rhs = tuple(sorted(rhs))
# Create new rule object and yield it
yield Rule(a_m, rhs, count(l_k), count(a_m), count(rhs), num_transactions)
# If the left hand side has one item only, do not recurse the function
if len(a_m) <= 1:
continue
yield from _genrules(l_k, a_m, itemsets, min_conf, num_transactions)
[docs]
def generate_rules_apriori(
itemsets: typing.Dict[int, typing.Dict[tuple, int]],
min_confidence: float,
num_transactions: int,
verbosity: int = 0,
):
"""
Bottom up algorithm for generating association rules from itemsets, very
similar to the fast algorithm proposed in the original 1994 paper by
Agrawal et al.
The algorithm is based on the observation that for {a, b} -> {c, d} to
hold, both {a, b, c} -> {d} and {a, b, d} -> {c} must hold, since in
general conf( {a, b, c} -> {d} ) >= conf( {a, b} -> {c, d} ).
In other words, if either of the two one-consequent rules do not hold, then
there is no need to ever consider the two-consequent rule.
Parameters
----------
itemsets : dict of dicts
The first level of the dictionary is of the form (length, dict of item
sets). The second level is of the form (itemset, count_in_dataset)).
min_confidence : float
The minimum confidence required for the rule to be yielded.
num_transactions : int
The number of transactions in the data set.
verbosity : int
The level of detail printing when the algorithm runs. Either 0, 1 or 2.
Examples
--------
>>> itemsets = {1: {('a',): 3, ('b',): 2, ('c',): 1},
... 2: {('a', 'b'): 2, ('a', 'c'): 1}}
>>> list(generate_rules_apriori(itemsets, 1.0, 3))
[{b} -> {a}, {c} -> {a}]
"""
# Validate user inputs
if not ((0 <= min_confidence <= 1) and isinstance(min_confidence, numbers.Number)):
raise ValueError("`min_confidence` must be a number between 0 and 1.")
if not ((num_transactions >= 0) and isinstance(num_transactions, numbers.Number)):
raise ValueError("`num_transactions` must be a number greater than 0.")
def count(itemset):
"""
Helper function to retrieve the count of the itemset in the dataset.
"""
return itemsets[len(itemset)][itemset]
if verbosity > 0:
print("Generating rules from itemsets.")
# For every itemset of a perscribed size
for size in itemsets.keys():
# Do not consider itemsets of size 1
if size < 2:
continue
if verbosity > 0:
print(" Generating rules of size {}.".format(size))
# For every itemset of this size
for itemset in itemsets[size].keys():
# Generate combinations to start off of. These 1-combinations will
# be merged to 2-combinations in the function `_ap_genrules`
H_1 = []
# Special case to capture rules such as {others} -> {1 item}
for removed in itertools.combinations(itemset, 1):
# Compute the left hand side
remaining = set(itemset).difference(set(removed))
lhs = tuple(sorted(remaining))
# If the confidence is high enough, yield the rule
conf = count(itemset) / count(lhs)
if conf >= min_confidence:
yield Rule(
lhs,
removed,
count(itemset),
count(lhs),
count(removed),
num_transactions,
)
# Consider the removed item for 2-combinations in the function `_ap_genrules`
H_1.append(removed)
# If H_1 is empty, there is nothing for passing to _ap_genrules, so continue to the next itemset
if len(H_1) == 0:
continue
yield from _ap_genrules(itemset, H_1, itemsets, min_confidence, num_transactions)
if verbosity > 0:
print("Rule generation terminated.\n")
def _ap_genrules(
itemset: tuple,
H_m: typing.List[tuple],
itemsets: typing.Dict[int, typing.Dict[tuple, int]],
min_conf: float,
num_transactions: int,
):
"""
Recursively build up rules by adding more items to the right hand side.
This algorithm is called `ap-genrules` in the original paper. It is
called by the `generate_rules_apriori` generator above. See it's docs.
Parameters
----------
itemset : tuple
The itemset under consideration.
H_m : tuple
Subsets of the itemset of length m, to be considered for rhs of a rule.
itemsets : dict of dicts
All itemsets and counts for in the data set.
min_conf : float
The minimum confidence for a rule to be returned.
num_transactions : int
The number of transactions in the data set.
"""
def count(itemset):
"""
Helper function to retrieve the count of the itemset in the dataset.
"""
return itemsets[len(itemset)][itemset]
# If H_1 is so large that calling `apriori_gen` will produce right-hand
# sides as large as `itemset`, there will be no right hand side.
# This should not happen happen, so we return.
if len(itemset) <= (len(H_m[0]) + 1):
return
# Generate right-hand itemsets of length k + 1 if H is of length k
H_m = list(apriori_gen(H_m))
H_m_copy = H_m.copy()
# For every possible right hand side
for h_m in H_m:
# Compute the left hand side of the rule
lhs = tuple(sorted(set(itemset).difference(set(h_m))))
# If the confidence is high enough, yield the rule, else remove from
# the upcoming recursive generator call
if (count(itemset) / count(lhs)) >= min_conf:
yield Rule(
lhs,
h_m,
count(itemset),
count(lhs),
count(h_m),
num_transactions,
)
else:
H_m_copy.remove(h_m)
# Unless the list of right-hand sides is empty, recurse the generator call
if H_m_copy:
yield from _ap_genrules(itemset, H_m_copy, itemsets, min_conf, num_transactions)
if __name__ == "__main__":
import pytest
pytest.main(args=[".", "--doctest-modules", "-v"])
```