#!/usr/bin/python3
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as stats
import _tester_utils
def str_join(a, b, c):
a = np.array(a)
b = np.array(b)
c = np.array(c)
str_array = np.core.defchararray.add(np.core.defchararray.add(a, b), c)
return str_array
def get_sensivities(results_df, detector_name, experiment=None):
if experiment!=None:
tp_col_names = str_join(detector_name+' ', [experiment], ' TP')
fp_col_names = str_join(detector_name+' ', [experiment], ' FP')
fn_col_names = str_join(detector_name+' ', [experiment], ' FN')
tn_col_names = str_join(detector_name+' ', [experiment], ' TN')
total_tp = (results_df.loc[:, tp_col_names].values)[:,0]
total_fn = (results_df.loc[:, fn_col_names].values)[:,0]
else:
tp_col_names = detector_name+' '+'TP'
fp_col_names = detector_name+' '+'FP'
fn_col_names = detector_name+' '+'FN'
tn_col_names = detector_name+' '+'TN'
total_tp = results_df.loc[:, tp_col_names].values
total_fn = results_df.loc[:, fn_col_names].values
se = []
for tp,fn in zip(total_tp,total_fn):
if (tp + fn) > 0:
s = tp/(tp+fn)*100.0
se.append(s)
return np.array(se)
def get_result(results_df, det_names, experiment=None):
m = []
s = []
for det in det_names:
m.append(np.mean(get_sensivities(results_df, det, experiment)))
s.append(np.std(get_sensivities(results_df, det, experiment)))
return np.array(m),np.array(s)
def compare_det_test(results_df, detector_name1, detector_name2, experiment=None):
se1 = get_sensivities(results_df, detector_name1, experiment)
if len(se1) < 2:
return 0
se2 = get_sensivities(results_df, detector_name2, experiment)
if len(se2) < 2:
return 0
l = min(len(se1),len(se2))
if (l < 20):
return None
#print("1:",se1[:l])
#print("2:",se2[:l])
try:
t,p = stats.wilcoxon(se1[:l],se2[:l])
return p
except:
return None
def compare_cables(results_df1,results_df2,det1,experiment):
se1 = get_sensivities(results_df1, det1, experiment)
if len(se1) < 2:
return 0
se2 = get_sensivities(results_df2, det1, experiment)
if len(se2) < 2:
return 0
l = min(len(se1),len(se2))
if (l < 20):
return None
try:
t,p = stats.wilcoxon(se1[:l],se2[:l])
return p
except:
return None
def compare_experiments(results_df1,det1):
se1 = get_sensivities(results_df1, det1, 'sitting')
if len(se1) < 2:
return 0
se2 = get_sensivities(results_df1, det1, 'jogging')
if len(se2) < 2:
return 0
l = min(len(se1),len(se2))
if (l < 20):
return None
try:
t,p = stats.wilcoxon(se1[:l],se2[:l])
return p
except:
return None
def single_plot(data, std, y_label, title = None):
fig, ax = plt.subplots()
_tester_utils.det_fullnames = ['Elgendi et al', 'Matched Filter', 'Kalidas and Tamil', 'Engzee Mod', 'Christov', 'Hamilton', 'Pan and Tompkins']
x_pos = np.arange(len(_tester_utils.det_fullnames))
fig.set_size_inches(10, 7)
rects1 = ax.bar(x_pos, data, yerr=std, width = 0.65, align='center', alpha=0.5, ecolor='black', capsize=10)
ax.set_ylim([0,100])
ax.set_ylabel(y_label)
ax.set_xlabel('Detector')
ax.set_xticks(x_pos)
ax.set_xticklabels(_tester_utils.det_fullnames)
if title!=None:
ax.set_title(title)
plt.tight_layout()
return rects1
def double_plot(data1, std1, data2, std2, y_label, legend1, legend2, title=None):
fig, ax = plt.subplots()
_tester_utils.det_fullnames = ['Elgendi et al', 'Matched Filter', 'Kalidas and Tamil', 'Engzee Mod', 'Christov', 'Hamilton', 'Pan and Tompkins']
x_pos = np.arange(len(_tester_utils.det_fullnames))
fig.set_size_inches(10, 7)
width = 0.4
rects1 = ax.bar(x_pos, data1, width, yerr=std1, alpha=0.5, ecolor='black', capsize=10)
rects2 = ax.bar(x_pos+width, data2, width, yerr=std2, alpha=0.5, ecolor='black', capsize=10)
ax.set_ylim([0,100])
ax.set_ylabel(y_label)
ax.set_xlabel('Detector')
ax.set_xticks(x_pos + width / 2)
ax.set_xticklabels(_tester_utils.det_fullnames)
ax.legend((rects1[0], rects2[0]), (legend1, legend2))
if title!=None:
ax.set_title(title)
plt.tight_layout()
return rects1, rects2
def print_stat(p):
if p == None:
print('--- & ',end='')
return
s = ""
if p < 0.05:
s = "*"
print('{:03.2f}{} & '.format(p,s),end='')
def print_detector_comparision_table(title,datafile,experiment=None):
print(title)
print(" & ",end='')
for detlong1 in _tester_utils.det_fullnames:
print(detlong1," & ",end='')
print("\\\\")
for det1,detlong1 in zip(_tester_utils.det_names,_tester_utils.det_fullnames):
print(detlong1," & ",end='')
for det2,detlong2 in zip(_tester_utils.det_names,_tester_utils.det_fullnames):
p = compare_det_test(datafile, det1, det2, experiment)
print_stat(p)
print("\\\\")
print()
# GUDB
gudb_cs_results = pd.read_csv('results_GUDB_chest_strap.csv', dtype=int, index_col=0)
gudb_cable_results = pd.read_csv('results_GUDB_loose_cables.csv', dtype=int, index_col=0)
# MITDB
mitdb_results = pd.read_csv('results_MITDB.csv', dtype=int, index_col=0)
experiment_names = ['sitting','maths','walking','hand_bike','jogging']
output_names = ['TP', 'FP', 'FN', 'TN']
print_detector_comparision_table("MIT database",mitdb_results)
print_detector_comparision_table("EINTHOVEN SITTING",gudb_cable_results,'sitting')
print_detector_comparision_table("CHEST STRAP SITTING",gudb_cs_results,'sitting')
print_detector_comparision_table("EINTHOVEN JOGGING",gudb_cable_results,'jogging')
print_detector_comparision_table("CHEST STRAP JOGGING",gudb_cs_results,'jogging')
print("\n\n\n")
print("CHEST STRAP VS EINTH")
for exp1 in ['sitting','jogging']:
print(exp1,":")
for det1 in _tester_utils.det_names:
p = compare_cables(gudb_cable_results,gudb_cs_results,det1,exp1)
print_stat(p)
print("\\\\")
print("\n\n\n")
print("LOOSE CABLES: SITTING VS JOGGING")
for det1 in _tester_utils.det_names:
p = compare_experiments(gudb_cable_results,det1)
print_stat(p)
print("\\\\")
print("\n\n\n")
print("CHEST STRAP: SITTING VS JOGGING")
for det1 in _tester_utils.det_names:
p = compare_experiments(gudb_cs_results,det1)
print_stat(p)
print("\\\\")
print("\n\n\n")
# calculating results
mitdb_avg,mitdb_std = get_result(mitdb_results, _tester_utils.det_names)
print("mitdb:",mitdb_avg)
gudb_cs_sitting_avg,gudb_cs_sitting_std = get_result(gudb_cs_results,
_tester_utils.det_names, 'sitting')
print("chest strap sitting:",gudb_cs_sitting_avg)
gudb_cable_sitting_avg,gudb_cable_sitting_std = get_result(gudb_cable_results,
_tester_utils.det_names, 'sitting')
print("lose cables sitting:",gudb_cable_sitting_avg)
gudb_cs_jogging_avg,gudb_cs_jogging_std = get_result(gudb_cs_results,
_tester_utils.det_names, 'jogging')
gudb_cable_jogging_avg,gudb_cable_jogging_std = get_result(gudb_cable_results,
_tester_utils.det_names, 'jogging')
# plotting
single_plot(mitdb_avg, mitdb_std, 'Sensitivity (%)', 'MITDB')
double_plot(gudb_cs_sitting_avg, gudb_cs_sitting_std,
gudb_cable_sitting_avg, gudb_cable_sitting_std,
'Sensitivity (%)', 'Chest Strap', 'Loose Cables', 'GUDB: cable, sitting')
double_plot(gudb_cs_jogging_avg, gudb_cs_jogging_std,
gudb_cable_jogging_avg, gudb_cable_jogging_std,
'Sensitivity (%)', 'Chest Strap', 'Loose Cables', 'GUDB: cable, jogging')
plt.show()
