QuantileTransform

class geoanalytics.normalization.QuantileTransform.QuantileTransform(dataframe)

Bases: object

About this algorithm

Description:

Quantile Transformation maps the data to a normal distribution by transforming features to follow a uniform or normal distribution. This technique is robust to outliers and useful for many machine learning algorithms requiring normally distributed data. This class supports runtime and memory usage tracking.

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 QuantileTransform

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

scaler = QuantileTransform(df)

normalized_df = scaler.run()

scaler.getRuntime()

scaler.getMemoryUSS()

scaler.getMemoryRSS()

scaler.save("QuantileTransform.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 quantile transformation on the dataset.

Returns:

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

Return type:

pd.DataFrame

save(outputFile='QuantileTransform.csv')

Saves the Normalized DataFrame to a CSV file.