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example.py
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example.py
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#!/usr/bin/env python
# encoding: utf-8
"""
example.py
Created by Ben Birnbaum on 2012-12-02.
Example use of outlierdetect.py module.
"""
from __future__ import print_function
from matplotlib import mlab
import outlierdetect
import pandas as pd
DATA_FILE = 'example_data.csv'
QUESTIONS = ['cough', 'fever']
def compute_mma(data):
# Compute MMA outlier scores.
(mma_scores, _) = outlierdetect.run_mma(data, 'interviewer_id', QUESTIONS)
print("\nMMA outlier scores")
print_scores(mma_scores)
def compute_sva(data):
# Compute SVA outlier scores.
(sva_scores, _) = outlierdetect.run_sva(data, 'interviewer_id', QUESTIONS)
print("\nSVA outlier scores")
print_scores(sva_scores)
def print_scores(scores):
for interviewer in scores.keys():
print("%s" % interviewer)
for column in scores[interviewer].keys():
score = scores[interviewer][column]['score']
print("Question: %s" % column)
print("Score: %d" % score)
# Uncomment the following to print additional information about each outlier:
# observed_frequencies = scores[interviewer][column]['observed_freq']
# expected_frequencies = scores[interviewer][column]['expected_freq']
# p_value = scores[interviewer][column]['p_value']
# print("Observed Frequencies: %s" % observed_frequencies)
# print("Expected Frequencies: %s" % expected_frequencies)
# print("P-Value: %d" % p_value)
if __name__ == '__main__':
data = pd.read_csv(DATA_FILE) # Uncomment to load as pandas.DataFrame.
# data = mlab.csv2rec(DATA_FILE) # Uncomment to load as numpy.recarray.
# Compute MMA outlier scores. Will work only if scipy is installed.
if hasattr(outlierdetect, 'run_mma'):
compute_mma(data)
# compute_sva(data) # Uncomment to use the SVA algorithm.