KNNImputation

class geoanalytics.imputation.KNNImputation.KNNImputation(dataframe)

Bases: object

About this algorithm

Description:

K-Nearest Neighbors (KNN) Imputation estimates missing values by finding the k nearest samples (rows) in the dataset and imputing missing values based on the average (or weighted average) of those neighbors’ corresponding feature values.

Parameters:

• Dataset (pandas DataFrame) must be provided during object initialization.

• Number of neighbors n_neighbors is specified during the run() call.

Attributes:

• df (pd.DataFrame) – The input data with ‘x’, ‘y’ coordinates and features.

• imputedDF (pd.DataFrame) – DataFrame after filling in missing values.

• startTime, endTime (float) – Variables to track execution time.

• memoryUSS, memoryRSS (float) – Memory usage of the imputation process in kilobytes.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.imputation import KNNImputation

df = pd.read_csv("input.csv")

knn_imputer = KNNImputation(df)

imputed_df = knn_imputer.run(n_neighbors=5)

knn_imputer.getRuntime()
knn_imputer.getMemoryUSS()
knn_imputer.getMemoryRSS()

knn_imputer.save('KNN.csv')

Credits

This implementation was created by Raashika and revised by M.Charan Teja under the guidance of Professor Rage Uday Kiran.

getMemoryRSS()

Prints the memory usage (RSS) of the process in kilobytes.

getMemoryUSS()

Prints the memory usage (USS) of the process in kilobytes.

getRuntime()

Prints the total runtime of the clustering algorithm.

run(n_neighbors=5)

Executes the KNN Imputation algorithm by replacing missing values based on nearest neighbor averages.

Parameters:

n_neighbors – int, number of neighbors to use for imputation (default: 5)

Returns:

imputedDF (pd.DataFrame) – DataFrame with missing values filled

save(outputFile='KNN.csv')

Saves the imputed DataFrame to a CSV file.

Parameters:

outputFile – str, filename to save the imputed data (default: ‘KNN.csv’)