GitLocker: The Coding Marketplace

Description:

pyECLAT 1.0.2

pyECLAT
Unlike the a priori method, the ECLAT method is not based on the calculation of confidence and lift, therefore the ECLAT method is based on the calculation of the support conjunctions of the variables.
pyECLAT is a simple package for associating variables based on the support of the different items of a dataframe.This method returns two dictionaries, one with the frequency of occurrence of the items conjunctions and the other with the support of the items conjunctions.
Install
Via pip
pip3 install pyECLAT

Via github
git clone https://github.com/jeffrichardchemistry/pyECLAT
cd pyECLAT
python3 setup.py install

Dependencies

numpy>=1.17.4, pandas>=0.25.3, tqdm>=4.41.1

How to use
This package has two dataframes as example, its possible to use:
from pyECLAT import Example1, Example2
ex1 = Example1().get()
ex2 = Example2().get()

The working dataframe should look like the one below. In this case, each line represents a customer's purchase at a supermarket.

0
1
2
3

0
milk
beer
bread
butter

1
coffe
bread
butter
NaN

2
coffe
bread
butter
NaN

3
milk
coffe
bread
butter

4
beer
NaN
NaN
NaN

5
butter
NaN
NaN
NaN

6
bread
NaN
NaN
NaN

7
bean
NaN
NaN
NaN

8
rice
bean
NaN
NaN

9
rice
NaN
NaN
NaN

This package works directly with a pandas dataframe without column's name.
Example: Making your dataframe
import pandas as pd
dataframe = pd.read_csv('dir/of/file.csv', header=None)

Run ECLAT method:
from pyECLAT import ECLAT
eclat_instance = ECLAT(data=dataframe, verbose=True) #verbose=True to see the loading bar

After getting eclat_instance, a binary dataframe is automatically generated, among other resources that can be accessed:
eclat_instance.df_bin #generate a binary dataframe, that can be used for other analyzes.
eclat_instance.uniq_ #a list with all the names of the different items

eclat_instance.support, eclat_instance.fit and eclat_instance.fit_all are the functions to perform the calculations. Example:
get_ECLAT_indexes, get_ECLAT_supports = eclat_instance.fit(min_support=0.08,
min_combination=1,
max_combination=3,
separator=' & ',
verbose=True)

It is possible to access the documentation, as well as the description, of each method using:
help(eclat_instance.fit)
help(eclat_instance.fit_all)
help(eclat_instance.support)