MICEImputation

class geoanalytics.imputation.MICEImputation.MICEImputation(dataframe)

Bases: object

About this algorithm

Description:

MICEImputation uses fancyimpute.IterativeImputer to perform MICE-based missing data imputation via chained equations. It excludes ‘x’ and ‘y’ during imputation and reports runtime and memory usage.

Parameters:

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

Attributes:

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

• imputedDF (pandas.DataFrame) – Final DataFrame after applying MICE 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 MICEImputation as alg

import pandas as pd

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

obj = alg.MICEImputation(df)

imputed_df = obj.run()

obj.save('MICEImputation.csv')

obj.getRuntime()

obj.getMemoryUSS()

obj.getMemoryRSS()

print("Data after MICE 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 algorithm.

run()

Executes MICE 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.

save(outputFile='MICE.csv')

Saves the imputed DataFrame to a CSV file.