"""
from matplotlib import mlab
import outlierdetect
import pandas as pd
DATA_FILE = 'example_data.csv'
def print_scores(scores):
for interviewer in scores.keys():
print "%s" % interviewer
for column in scores[interviewer].keys():
print "\t%s:\t%.2f" % (column, scores[interviewer][column])
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 SVA outlier scores.
sva_scores = outlierdetect.run_sva(data, 'interviewer_id', ['cough', 'fever'])
print "SVA outlier scores"
print_scores(sva_scores)
# Compute MMA outlier scores. Will work only if scipy is installed.
mma_scores = outlierdetect.run_mma(data, 'interviewer_id', ['cough', 'fever'])
print "\nMMA outlier scores"
print_scores(mma_scores)
"""
from matplotlib import mlab
import outlierdetect
import pandas as pd
DATA_FILE = 'example_data.csv'
def print_scores(scores):
for interviewer in scores.keys():
print "%s" % interviewer
for column in scores[interviewer].keys():
print "\t%s:\t%.2f" % (column, scores[interviewer][column])
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 SVA outlier scores.
(sva_scores, agg_col_to_data) = outlierdetect.run_sva(data, 'interviewer_id', ['cough', 'fever'])
print "SVA outlier scores"
print_scores(sva_scores)
# Compute MMA outlier scores. Will work only if scipy is installed.
(mma_scores, agg_col_to_data) = outlierdetect.run_mma(data, 'interviewer_id', ['cough', 'fever'])
print "\nMMA outlier scores"
print_scores(mma_scores)
def print_scores(scores):
for interviewer in scores.keys():
print("%s" % interviewer)
for column in scores[interviewer].keys():
score = scores[interviewer][column]['score']
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 SVA outlier scores.
(sva_scores, _) = outlierdetect.run_sva(data, 'interviewer_id', ['cough', 'fever'])
print("SVA outlier scores")
print_scores(sva_scores)
# Compute MMA outlier scores. Will work only if scipy is installed.
if hasattr(outlierdetect, 'run_mma'):
(mma_scores, _) = outlierdetect.run_mma(data, 'interviewer_id', ['cough', 'fever'])
print("\nMMA outlier scores")
print_scores(mma_scores)
