Frequent Itemset Mining and Frequent Sequence Mining Algorithms package for ARM
Project description
FIMProject - Frequent Itemset Algorithms and Frequent Sequence Mining Algorithms in Python
Installation
FIMProject requires Python 3.7 or newer, and the easiest way to install it is via
pip
:
pip install FIMProject
Simple Example
from FIM import apriori
from FIM import association_rules
from FIM.utils import TransactionEncoder
# The apriori function expects data in a one-hot encoded pandas DataFrame.
# Suppose we have the following transaction data:
data = [['onion', 'beer', 'crisps', 'beef'],
['beer', 'tomato', 'crisps', 'eggs'],
['onion', 'crisps', 'eggs'],
['beer', 'eggs', 'beef'],
['onion', 'beer', 'carrot', 'crisps'],
['onion', 'eggs', 'beef'],
['onion', 'beer', 'carrot', 'crisps', 'eggs', 'beef'],
['onion', 'beer', 'crisps', 'eggs'],
['beer', 'tomato', 'carrot', 'eggs'],
['onion', 'crisps', 'eggs', 'beef'],
['beer', 'carrot', 'crisps', 'eggs']]
# We can transform it into the right format via the TransactionEncoder as follows:
te = TransactionEncoder()
df = te.fit_transform(data, set_pandas=True)
# Now, let us return the items and itemsets with at least 30% support:
freq_items = apriori(df, min_support=0.3)
# Now, let us return the association rules with freq_items df:
rules = association_rules(freq_items, metric="confidince", min_threshold=0.7)
What is df?
df is a pandas dataframe. It is a table of transactions. Each row is a transaction and each column is an item. The value of each cell is the number of items in the transaction.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
fimproject-0.0.7.tar.gz
(9.9 kB
view hashes)
Built Distribution
fimproject-0.0.7-py3-none-any.whl
(12.4 kB
view hashes)
Close
Hashes for fimproject-0.0.7-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 107d34edc36c82412053dd32df33c92fc5abf2be296723d5919f717afb10ab32 |
|
MD5 | b3bb35b478d33d8b2a77759e5f3c4190 |
|
BLAKE2b-256 | ce5b0f324732e3bf085a793a0b6df0be795423a28b70bb27e81cc8c0b27d76c9 |