lshkcenters 1.0.1

A python package for LSH-k-Centers algorithm

Python implementations of the LSH-k-Representatives algorithms for clustering categorical data:

Different from k-Modes algorithm, LSH-k-Representatives define the "representatives" that keep the frequencies of all categorical values of the clusters.

Installation:

Using pip:

pip install lshkrepresentatives

Import the packages:

import numpy as np
from LSHkRepresentatives.LSHkRepresentatives_Init import LSHkRepresentatives_Init
from LSHkRepresentatives.LSHkRepresentatives_Full import LSHkRepresentatives_Full

Generate a simple categorical dataset:

X = np.array([[0,0,0],[0,1,1],[0,0,0],[1,0,1],[2,2,2],[2,3,2],[2,3,2]])
y = np.array([0,0,0,0,1,1,1])

LSHk-Representatives (Init):

kreps = LSHkRepresentatives_Init(X,y,n_init=5,n_clusters=2 ,verbose=3)
kreps.SetupLSH()
kreps.DoCluster()

Built-in evaluattion metrics:

kreps.CalcScore()

Out come:

Generating disMatrix for DILCA
Saving DILCA to: saved_dist_matrices/json/DILCA_None.json
Generating LSH hash table:   hbits: 2(4)  k 2  d 3  n= 7
LSH time: 0.016015699999999633 Score:  6.333333333333334  Time: 0.0019595600000000602
Purity: 1.00 NMI: 1.00 ARI: 1.00 Sil:  0.59 Acc: 1.00 Recall: 1.00 Precision: 1.00

LSHk-Representatives (Full):

kreps = LSHkRepresentatives_Full(X,y,n_init=5,n_clusters=2 ,verbose=3)
kreps.SetupLSH()
kreps.DoCluster()

Built-in evaluattion metrics:

kreps.CalcScore()

Out come:

SKIP LOADING distMatrix because: True bd=None
Generating disMatrix for DILCA
Saving DILCA to: saved_dist_matrices/json/DILCA_None.json
Generating LSH hash table:   hbits: 2(4)  k 2  d 3  n= 7
 n_group=2 Average neighbors:1.0
LSH time: 0.00661619999999985 Score:  6.333333333333334  Time: 0.000932080000000024
Purity: 1.00 NMI: 1.00 ARI: 1.00 Sil:  0.59 Acc: 1.00 Recall: 1.00 Precision: 1.00

Parameters:

X: Categorical dataset
y: Labels of object (for evaluation only)
n_init: Number of initializations
n_clusters: Number of target clusters
max_iter: Maximum iterations
verbose:
random_state:

If the variable MeasureManager.IS_LOAD_AUTO is set to "True": The DILCA will get the pre-caculated matrix

Outputs:

cluster_representatives: List of final representatives
labels_: Prediction labels
cost_: Final sum of squared distance from objects to their centroids
n_iter_: Number of iterations
epoch_costs_: Average time for an initialization

LSH-k-Representatives: To be updated

References:

To be updated