MultipleImputation

class geoanalytics.imputation.MultipleImputation.MultipleImputation(dataframe)

Bases: object

About this algorithm

Description:

MultipleImputation performs iterative multivariate imputation using chained equations with linear regression to estimate missing values based on relationships among features.

Parameters:

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

• Additional tuning parameters can be provided during the run() call.

Attributes:

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

• imputedDF (pd.DataFrame) – DataFrame containing ‘x’, ‘y’, and imputed values.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.imputation import MultipleImputation

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

mi = MultipleImputation(df)

output = mi.run()

mi.save('MultipleImputaion.csv')

Credits

This implementation was created by and revised by 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_nearest_features=None, max_iter=10, random_state=0)

Executes iterative multivariate imputation using linear regression.

Parameters:

• n_nearest_features – int or None, optional Number of features to use when estimating missing values. If None, all features are used.

• max_iter – int, default=10 Maximum number of imputation iterations.

• random_state – int, default=0 Seed for reproducibility.

Returns:

pandas DataFrame with imputed values and original ‘x’, ‘y’ columns.

save(outputFile='MultipleImputation.csv')

Saves the imputed DataFrame to a CSV file.

Parameters:

outputFile – Filename to save the resulting DataFrame.