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https://github.com/remykarem/apriori-rs
Apriori for association rule mining with Python bindings 🦀🐍
https://github.com/remykarem/apriori-rs
apriori association-rule-mining market-basket-analysis python rust
Last synced: about 1 month ago
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Apriori for association rule mining with Python bindings 🦀🐍
- Host: GitHub
- URL: https://github.com/remykarem/apriori-rs
- Owner: remykarem
- License: mit
- Created: 2021-09-16T00:14:53.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2021-10-09T17:24:59.000Z (about 3 years ago)
- Last Synced: 2024-08-01T16:38:41.369Z (3 months ago)
- Topics: apriori, association-rule-mining, market-basket-analysis, python, rust
- Language: Rust
- Homepage:
- Size: 6.59 MB
- Stars: 11
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# apriori-rs
Implementation of apriori algorithm for association rule mining. Written in Rust 🦀 with Python bindings.
## Installation
First install Rust
```sh
curl https://sh.rustup.rs -sSf | sh -s -- -y
```
then install the package
```sh
pip install git+https://github.com/remykarem/apriori-rs.git
```
To compile the module yourself (macOS),
```sh
cargo rustc --release -- -C link-arg=-undefined -C link-arg=dynamic_lookup && mv target/release/libapriori.dylib ./apriori.so
```
## Usage
### Generating frequent itemsets
Prepare the data as a list of sets of strings.
```python
>>> from apriori import generate_frequent_itemsets
>>> transactions = [
... set(["bread", "milk", "cheese"]),
... set(["bread", "milk"]),
... set(["milk", "cheese", "bread"]),
... set(["milk", "cheese", "bread"]),
... set(["milk", "cheese", "yoghurt"]),
... set(["milk", "bread"])]
```
Then
```python
>>> itemsets, id2item = generate_frequent_itemsets(transactions, min_support=0.5, max_length=3)
>>> itemsets[1]
{frozenset({2}): 4, frozenset({0}): 5, frozenset({1}): 6}
>>> itemsets[2]
{frozenset({0, 1}): 5, frozenset({1, 2}): 4, frozenset({0, 2}): 3}
>>> itemsets[3]
{frozenset({0, 1, 2}): 3}
>>> id2item
{2: 'cheese', 0: 'bread', 3: 'yoghurt', 1: 'milk'}
```
Use `generate_frequent_itemsets_id` if your items are indices.
### Association rules
```python
>>> rules, counts = apriori(
... transactions,
... min_support=0.3,
... min_confidence=0.2,
... max_length=3)
```
```python
>>> rules
[{"cheese", "bread"} -> {"milk"},
{"cheese"} -> {"milk"},
{"bread"} -> {"milk"},
{"milk"} -> {"bread"},
{"milk", "cheese"} -> {"bread"},
{"cheese"} -> {"bread", "milk"},
{"cheese"} -> {"bread"},
{"milk"} -> {"cheese"},
{"bread", "milk"} -> {"cheese"},
{"bread"} -> {"milk", "cheese"},
{"bread"} -> {"cheese"},
{"milk"} -> {"cheese", "bread"}]
```
Obtain confidence and lift for a rule.
```python
>>> rules[0]
{"bread", "cheese"} -> {"milk"}
>>> rules[0].confidence
1.0
>>> rules[0].lift
1.0
```
## Benchmarks
Time taken (s) to generate frequent itemsets for the Online Retail II dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/00502/) given minimum support and maximum length of itemset.
| Min support, length | apriori-rs | [efficient-apriori](https://github.com/tommyod/Efficient-Apriori) | [mlxtend](http://rasbt.github.io/mlxtend/user_guide/frequent_patterns/apriori/) | [apyori](https://github.com/ymoch/apyori) |
|:-------------------:|:----------:|:----------------:|:-------:|:----:|
| 0.100, 1 | 0.2s | 0.1s | 0.1s | 0.29s
| 0.100, 2 | 0.2s | 0.1s | 0.1s | 0.26s
| 0.100, 3 | 0.2s | 0.1s | 0.1s | 0.25s
| 0.100, 4 | 0.2s | 0.1s | 0.1s |0.25s
| 0.100, 5 | 0.2s | 0.1s | 0.1s | 0.25s
| 0.050, 1 | 0.2s | 0.1s | 0.1s | 0.25s
| 0.050, 2 | 0.2s | 0.2s | 0.1s |0.25s
| 0.050, 3 | 0.2s | 0.2s | 0.1s | 0.25s
| 0.050, 4 | 0.2s | 0.2s | 0.1s | 0.25s
| 0.050, 5 | 0.2s | 0.2s | 0.2s |0.25s
| 0.010, 1 | 0.2s | 0.1s | 0.1s | 0.32s
| 0.010, 2 | 16s | 261s | 73s | **2.1s**
| 0.010, 3 | 15s | 272s | 79s | **2.3s**
| 0.010, 4 | 17s | 284s | 78s | **2.4s**
| 0.010, 5 | 14s | 279s | 92s | **2.4s**
| 0.005, 1 | 0.2s | 0.1s | 0.1s | 0.25s
| 0.005, 2 | 76s | 1190s | 327s | **5.7s**
| 0.005, 3 | 68s | 1278s | 643s | **20s**
| 0.005, 4 | 81s | 1168s | 638s | **39s**
| 0.005, 5 | 70s | 1217s | 643s | **41s**
Benchmark was carried out on macOS Big Sur (11.6); 2.7 GHz Quad-Core Intel Core i7. Python version 3.8.11.