RootTransformation

class geoanalytics.normalization.RootTransformation.RootTransformation(dataframe, root=2)

Bases: object

About this algorithm

Description:

RootTransformation applies a root-based transformation to all feature values in the input dataset. This is useful for reducing the effect of large outliers and compressing the range of high magnitude values. For example, a square root (root=2) transformation is commonly used to stabilize variance in skewed datasets.

Parameters:

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

• root (int, optional): Degree of the root transformation. Defaults to 2 (square root).

Attributes:

• df (pd.DataFrame): Original input DataFrame with renamed first two columns as ‘x’ and ‘y’.

• normalizedDF (pd.DataFrame): DataFrame containing root-transformed features.

• startTime, endTime (float): Execution timestamps.

• memoryUSS, memoryRSS (float): Memory usage statistics in KB.

Execution methods

Calling from a Python program

import pandas as pd

from geoanalytics.normalization import RootTransformation

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

transformer = RootTransformation(df, root=3)

normalized_df = transformer.run()

transformer.getRuntime()

transformer.getMemoryUSS()

transformer.getMemoryRSS()

transformer.save("RootTransformation.csv")

Credits

Developed by Raashika and M. Charan Teja, supervised by 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 root transformation on the dataset.

Returns:

DataFrame with ‘x’, ‘y’, and root-transformed features.

Return type:

pd.DataFrame

save(outputFile='RootTransformation.csv')

Saves the Normalized DataFrame to a CSV file.