HotDeck

class geoanalytics.imputation.HotDeck.HotDeck(dataframe)

Bases: object

About this algorithm

Description:

Hot Deck Imputation is a simple, non-parametric method for handling missing values by replacing each missing entry with a randomly selected observed value from the same column. This method is suitable for datasets where missing values are not patterned or large in volume.

Parameters:

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

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 HotDeck

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

imputer = HotDeck(df)

imputed_df = imputer.run()

imputer.getRuntime()
imputer.getMemoryUSS()
imputer.getMemoryRSS()

imputer.save('HotDeck.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()

Executes the Hot Deck Imputation algorithm by replacing missing values with randomly selected non-missing values from the same column.

Returns:

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

save(outputFile='HotDeck.csv')

Saves the imputed DataFrame to a CSV file.

Parameters:

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