DataCentricKMeans 0.7

A package for running DataCentricKMeans.

#Authors:
    - Vasfi Tataroglu ([e-mail])
    - Parichit Sharma ([e-mail])
    - Hasan Kurban ([e-mail])
    - Mehmet M. Dalkilic ([e-mail])

DataCentricKMeans Package Documentation

Installation

First, you can install the package from PyPI:

pip install DataCentricKMeans

Usage

This package allows you to run various KMeans algorithms using the provided functions. All functions take similar parameters and return similarly structured results.

run_geokmeans Function

Function Definition:

def run_geokmeans(num_iterations, threshold, num_clusters, seed=None, file_paths=[])

Parameters:

1. num_iterations (int):

   • Description: Maximum number of iterations for the algorithm.
   • Condition: Must be 1 or greater.

2. threshold (float):

   • Description: Convergence threshold for the algorithm.
   • Condition: Must be 0 or greater.

3. num_clusters (int):

   • Description: The number of clusters.
   • Condition: Must be 2 or greater and less than the number of samples in the data.

4. seed (int, optional):

   • Description: The seed value for the random number generator. If not provided, the default value of 42 is used.
   • Condition: Must be a positive integer.

5. file_paths (list of str):

   • Description: The file paths of the data files to be processed.
   • Condition: The file paths must be valid and the files must exist.

Example Usage:

import DataCentricKMeans

results = DataCentricKMeans.run_geokmeans(
    100,
    0.0001,
    12,
    17,
    [
        "./Breastcancer.csv",
        "./CreditRisk.csv",
        "./census.csv",
        "./birch.csv"
    ]
)

# Accessing results
print(results[0].loop_counter)
print(results[0].to_dict())

Outputs: The run_geokmeans function returns a list of KMeansResult objects, one for each data file. Each KMeansResult object contains the following information:

• loop_counter (int): The number of iterations the algorithm ran.
• num_dists (int): The number of distances computed.
• assignments (list of int): The cluster assignment for each data point.
• centroids (list of list of float): The coordinates of each cluster's centroid.
• sse (float): The sum of squared errors (SSE).

Accessing the Results: You can access the results either as class attributes or as dictionary keys:

result = results[0]
print(result.loop_counter)  # Number of iterations
print(result.to_dict())     # All results as a dictionary

run_lloyd_kmeans Function

Function Definition:

def run_lloyd_kmeans(num_iterations, threshold, num_clusters, seed=None, file_paths=[])

The run_lloyd_kmeans function takes the same parameters as run_geokmeans and follows a similar structure. Its usage and outputs are the same.

Example Usage:

import DataCentricKMeans

results = DataCentricKMeans.run_lloyd_kmeans(
    100,
    0.0001,
    12,
    17,
    [
        "./Breastcancer.csv",
        "./CreditRisk.csv",
        "./census.csv",
        "./birch.csv"
    ]
)

# Accessing results
print(results[0].loop_counter)
print(results[0].to_dict())

run_elkan_kmeans Function

Function Definition:

def run_elkan_kmeans(num_iterations, threshold, num_clusters, seed=None, file_paths=[])

The run_elkan_kmeans function takes the same parameters as run_geokmeans and follows a similar structure. Its usage and outputs are the same.

Example Usage:

import DataCentricKMeans

results = DataCentricKMeans.run_elkan_kmeans(
    100,
    0.0001,
    12,
    17,
    [
        "./Breastcancer.csv",
        "./CreditRisk.csv",
        "./census.csv",
        "./birch.csv"
    ]
)

# Accessing results
print(results[0].loop_counter)
print(results[0].to_dict())

run_hamerly_kmeans Function

Function Definition:

def run_hamerly_kmeans(num_iterations, threshold, num_clusters, seed=None, file_paths=[])

The run_hamerly_kmeans function takes the same parameters as run_geokmeans and follows a similar structure. Its usage and outputs are the same.

Example Usage:

import DataCentricKMeans

results = DataCentricKMeans.run_hamerly_kmeans(
    100,
    0.0001,
    12,
    17,
    [
        "./Breastcancer.csv",
        "./CreditRisk.csv",
        "./census.csv",
        "./birch.csv"
    ]
)

# Accessing results
print(results[0].loop_counter)
print(results[0].to_dict())

run_annulus_kmeans Function

Function Definition:

def run_annulus_kmeans(num_iterations, threshold, num_clusters, seed=None, file_paths=[])

The run_annulus_kmeans function takes the same parameters as run_geokmeans and follows a similar structure. Its usage and outputs are the same.

Example Usage:

import DataCentricKMeans

results = DataCentricKMeans.run_annulus_kmeans(
    100,
    0.0001,
    12,
    17,
    [
        "./Breastcancer.csv",
        "./CreditRisk.csv",
        "./census.csv",
        "./birch.csv"
    ]
)

# Accessing results
print(results[0].loop_counter)
print(results[0].to_dict())

run_exponion_kmeans Function

Function Definition:

def run_exponion_kmeans(num_iterations, threshold, num_clusters, seed=None, file_paths=[])

The run_exponion_kmeans function takes the same parameters as run_geokmeans and follows a similar structure. Its usage and outputs are the same.

Example Usage:

import DataCentricKMeans

results = DataCentricKMeans.run_exponion_kmeans(
    100,
    0.0001,
    12,
    17,
    [
        "./Breastcancer.csv",
        "./CreditRisk.csv",
        "./census.csv",
        "./birch.csv"
    ]
)

# Accessing results
print(results[0].loop_counter)
print(results[0].to_dict())

Detailed Output Description

Each KMeansResult object contains the following attributes:

• loop_counter: Indicates the number of iterations the algorithm ran.
• num_dists: The total number of distances computed.
• assignments: A list indicating the cluster assignment for each data point.
• centroids: A list of lists, where each inner list contains the coordinates of a cluster centroid.
• ballkm_centroids: Ball KMeans centroid information in string format.
• timeout: A boolean indicating if the algorithm timed out.
• sse: The sum of squared errors (SSE).

Example of Processing Results

# Get results for the first data file
result = results[0]

# Print the number of iterations
print(f"Loop Counter: {result.loop_counter}")

# Print the SSE value
print(f"SSE: {result.sse}")

# Print all results as a dictionary
print(result.to_dict())

# Print centroid information
for centroid in result.centroids:
    print(centroid)

Summary

The DataCentricKMeans package provides easy-to-use functions for running various KMeans algorithms and accessing their results in Python. The examples and descriptions above illustrate how to use these functions and handle their outputs.