PowerTransform

class geoanalytics.normalization.PowerTransform.PowerTransform(dataframe)

Bases: object

About this algorithm

Description:

Power Transformation using the Yeo-Johnson method stabilizes variance and makes data more Gaussian-like. It is especially useful for features with non-normal distributions. This class provides runtime and memory usage tracking alongside the transformation.

Parameters:

• dataframe (pd.DataFrame): Input DataFrame with ‘x’, ‘y’ coordinates and feature columns.

Attributes:

• df (pd.DataFrame) – Input DataFrame with standardized column names ‘x’, ‘y’, and features.

• normalizedDF (pd.DataFrame) – DataFrame containing transformed features and coordinates.

• startTime, endTime (float) – Timing markers for the transformation process.

• memoryUSS, memoryRSS (float) – Memory usage metrics in kilobytes.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.normalization import PowerTransform

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

scaler = PowerTransform(df)

normalized_df = scaler.run()

scaler.getRuntime()

scaler.getMemoryUSS()

scaler.getMemoryRSS()

scaler.save("PowerTransform.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 Yeo-Johnson power transformation on the dataset.

Returns:

DataFrame with ‘x’, ‘y’, and power transformed feature columns.

Return type:

pd.DataFrame

save(outputFile='PowerTransform.csv')

Saves the Normalized DataFrame to a CSV file.