MedianImputation

class geoanalytics.imputation.MedianImputation.MedianImputation(dataframe)

Bases: object

About this algorithm

Description:

MedianImputation replaces missing values in feature columns with their respective medians, excluding ‘x’ and ‘y’ spatial columns, and reports runtime and memory usage after execution.

Parameters:

• dataframe (pandas.DataFrame) – Input dataset with ‘x’, ‘y’ spatial columns followed by numerical features with potential missing values.

Attributes:

• df (pandas.DataFrame) – Internal copy of the original input DataFrame with reordered columns.

• imputedDF (pandas.DataFrame) – Final DataFrame after applying median imputation.

• startTime (float) – Start time of the imputation.

• endTime (float) – End time of the imputation.

• memoryUSS (float) – Memory usage (USS in KB) during the run.

• memoryRSS (float) – Memory usage (RSS in KB) during the run.

Execution methods

Calling from a Python program

from geoanalytics.imputation import MedianImputation as alg

import pandas as pd

df = pd.read_csv('dataset.csv')

obj = alg.MedianImputation(df)

imputed_df = obj.run()

obj.save('MedianImputation.csv')

obj.getRuntime()

obj.getMemoryUSS()

obj.getMemoryRSS()

print("Data after Median Imputation:", imputed_df)

Credits

The complete program was written by and revised by under the supervision 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 median imputation on the dataset (excluding ‘x’ and ‘y’ columns), and returns the imputed DataFrame with original coordinates.

Returns:

imputedDFpandas.DataFrame

The DataFrame with missing values imputed using column medians.

save(outputFile='MedianImputation.csv')

Saves the imputed DataFrame to a CSV file.