phenome-outlier-analysis 0.1.0

A package for outlier detection in phenome datasets

phenome-outlier-analysis

OutlierDetector Class Documentation

Overview

The OutlierDetector class is designed for detecting outliers in datasets using various normalization methods. It supports both context-specific and global outlier detection strategies, making it versatile for different types of data analysis.

Class Initialization

OutlierDetector(df, analyte_columns, segment_columns=['sex'])

Parameters:

• df (pandas.DataFrame): The input DataFrame containing the data to be analyzed.
• analyte_columns (list): A list of column names to be analyzed for outliers.
• segment_columns (list, optional): A list of column names used for segmentation in context-specific outlier detection. Defaults to ['sex'].

Main Methods

1. perform_outlier_detection

perform_outlier_detection(lower_percentile=0.01, upper_percentile=0.99, method='double_mad', take_log=False)

This is the primary method to perform outlier detection on the given DataFrame.

Parameters:

• lower_percentile (float): Lower percentile for cutoff calculation. Default is 0.01.
• upper_percentile (float): Upper percentile for cutoff calculation. Default is 0.99.
• method (str): Normalization method. Can be 'double_mad' or 'zscore'. Default is 'double_mad'.
• take_log (bool): Whether to apply log transformation before normalization. Default is False.

Returns:

A tuple containing two dictionaries:

1. Context-specific results
2. Super-global results

2. context_specific_outlier_detection

context_specific_outlier_detection(method='double_mad', take_log=False)

Performs context-specific outlier detection by segmenting the DataFrame based on the segment_columns.

3. super_global_outlier_detection

super_global_outlier_detection(method='double_mad', take_log=False)

Evaluates outliers on a global scale, considering all data points together.

Helper Methods

calculate_double_mad

Calculates left and right Median Absolute Deviations (MADs) from the median.

normalize_series

Normalizes a series using the specified method (double_mad or zscore).

calculate_percentile_cutoffs

Calculates global percentile cutoffs based on the specified columns of a DataFrame.

create_binary_matrix

Creates a binary matrix indicating outliers based on specified cutoffs.

normalize_dataframe

Normalizes specified columns in a DataFrame.

detect_outliers

Detects outliers in the specified columns of a DataFrame.

get_global_cutoffs

Gets global cutoffs for outlier detection.

Usage Example

import pandas as pd
from outlier_detection import OutlierDetector

# Load your data
df = pd.read_csv('your_data.csv')

# Define columns
analyte_columns = ['column1', 'column2', 'column3']
segment_columns = ['sex', 'age_group']

# Create OutlierDetector instance
detector = OutlierDetector(df, analyte_columns, segment_columns)

# Perform outlier detection
context_results, global_results = detector.perform_outlier_detection(
    lower_percentile=0.01,
    upper_percentile=0.99,
    method='double_mad',
    take_log=True
)

# Analyze results
for (segment, value), result in context_results.items():
    print(f"Outliers for {segment}={value}:")
    print(result['binary_matrix'].sum())

print("Global outliers:")
print(global_results[('global', 'global')]['binary_matrix'].sum())

Notes

• The class uses logging to provide information and warnings during the outlier detection process.
• The tqdm library is used to show progress bars for long-running operations.
• The class can handle both context-specific (segmented) and global outlier detection.
• Two normalization methods are supported: 'double_mad' (double Median Absolute Deviation) and 'zscore'.
• Log transformation can be applied before normalization if needed.