In [1]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sbn
In [7]:
pwd
Out[7]:
u'/home/chiroptera/workspace/QCThesis/notebooks'
In [3]:
ls ../experiments/threshold/
all_stats.csv iris_stats.csv pima_stats.csv
house_votes.csv isolet.csv some_yeast_weird_results.csv
house_votes_stats.csv mfeat-fou.csv synthetic_control.csv
ionosphere.csv mfeat-fou_lifetime.csv synthetic_control_lifetime.csv
ionosphere_lifetime.csv optdigits.csv synthetic_control_stats.csv
ionosphere_stats.csv optdigits_lifetime.csv wine.csv
iris.csv pima.csv wine_stats.csv
iris_lifetime.csv pima_lifetime.csv
In [10]:
#folder = "/home/diogoaos/QCThesis/experiments/threshold/"
folder = "/home/chiroptera/QCThesis/experiments/threshold/"
name = "pima"
In [147]:
resPD = pd.read_csv(folder + name + ".csv")
In [148]:
n_samples=768
In [149]:
rounds = int(resPD.round.max() + 1)
irisStats = np.zeros((rounds, 5))
#threshold, n_assocs, assocs reduction
In [150]:
resPD.columns
Out[150]:
Index([u'threshold', u'max assoc', u'n assocs', u'accuracy', u'round'], dtype='object')
In [151]:
resPD
Out[151]:
threshold
max assoc
n assocs
accuracy
round
0
0.00
329
167140
0.645833
0
1
0.05
276
120960
0.645833
0
2
0.10
201
95960
0.645833
0
3
0.15
172
81060
0.645833
0
4
0.20
137
69858
0.645833
0
5
0.25
119
60564
0.645833
0
6
0.30
110
52232
0.645833
0
7
0.35
95
45326
0.515625
0
8
0.40
85
39064
0.515625
0
9
0.45
74
33800
0.515625
0
10
0.50
67
28798
0.515625
0
11
0.55
59
23968
0.515625
0
12
0.60
53
19558
0.515625
0
13
0.65
45
15620
0.515625
0
14
0.70
37
11966
0.649740
0
15
0.75
31
8890
0.649740
0
16
0.80
26
6490
0.649740
0
17
0.85
21
4400
0.643229
0
18
0.90
17
2694
0.649740
0
19
0.95
13
1442
0.648438
0
20
1.00
3
802
0.649740
0
21
0.00
342
178568
0.645833
1
22
0.05
264
120154
0.645833
1
23
0.10
207
93636
0.645833
1
24
0.15
167
78752
0.645833
1
25
0.20
144
67748
0.645833
1
26
0.25
117
58720
0.645833
1
27
0.30
105
50832
0.645833
1
28
0.35
90
43842
0.645833
1
29
0.40
80
37746
0.645833
1
...
...
...
...
...
...
390
0.60
52
20158
0.649740
18
391
0.65
47
15994
0.649740
18
392
0.70
38
12360
0.649740
18
393
0.75
31
9240
0.647135
18
394
0.80
26
6752
0.652344
18
395
0.85
23
4716
0.653646
18
396
0.90
18
2948
0.649740
18
397
0.95
17
1614
0.652344
18
398
1.00
5
844
0.649740
18
399
0.00
389
174512
0.645833
19
400
0.05
258
119444
0.645833
19
401
0.10
210
95224
0.645833
19
402
0.15
171
79946
0.645833
19
403
0.20
145
69050
0.645833
19
404
0.25
130
59984
0.645833
19
405
0.30
107
52034
0.645833
19
406
0.35
95
45152
0.515625
19
407
0.40
80
38962
0.515625
19
408
0.45
74
33792
0.515625
19
409
0.50
69
28658
0.515625
19
410
0.55
60
23886
0.515625
19
411
0.60
51
19466
0.515625
19
412
0.65
44
15486
0.648438
19
413
0.70
35
12054
0.648438
19
414
0.75
30
8868
0.649740
19
415
0.80
25
6392
0.649740
19
416
0.85
20
4374
0.648438
19
417
0.90
16
2666
0.651042
19
418
0.95
12
1418
0.649740
19
419
1.00
5
828
0.652344
19
420 rows × 5 columns
In [156]:
this_round = 0
full_assocs = resPD['n assocs'][0]
round_done = False
for i in range(2,resPD.shape[0]):
curr_round = resPD.round[i]
if curr_round != resPD.round[i-1]:
if not round_done:
irisStats[curr_round, 0] = 1
irisStats[curr_round, 1] = resPD['max assoc'][i-1]
irisStats[curr_round, 2] = resPD['n assocs'][i-1]
irisStats[curr_round, 3] = resPD['n assocs'][i-1] / full_assocs
full_assocs = resPD['n assocs'][i]
round_done = False
continue
if resPD.accuracy[i] != resPD.accuracy[i-1] and not round_done:
# if abs(resPD.accuracy[i] - resPD.accuracy[i-1]) > 0.01 and not round_done:
irisStats[curr_round, 0] = resPD.threshold[i-1]
irisStats[curr_round, 1] = resPD['max assoc'][i-1]
irisStats[curr_round, 2] = resPD['n assocs'][i-1]
irisStats[curr_round, 3] = resPD['n assocs'][i-1] / full_assocs
round_done = True
irisStats[:,4] = irisStats[:,2] / (n_samples**2)
In [157]:
columns = ["threshold", "max_assocs", "n_assocs", "n_assocs_pc", "density"]
irisStatsPD = pd.DataFrame(irisStats, columns=columns)
irisStatsPD
Out[157]:
threshold
max_assocs
n_assocs
n_assocs_pc
density
0
0.30
110
52232
0.312504
0.088555
1
0.70
35
11464
0.064200
0.019436
2
0.30
115
54418
0.321912
0.092261
3
0.35
100
45452
0.268804
0.077060
4
0.30
116
54126
0.288269
0.091766
5
0.35
90
45018
0.268992
0.076324
6
0.30
108
51462
0.306976
0.087250
7
0.30
114
50788
0.300407
0.086107
8
0.25
111
56418
0.342787
0.095652
9
0.35
105
48216
0.251723
0.081746
10
0.35
99
45300
0.252022
0.076803
11
0.65
42
15208
0.084069
0.025784
12
0.35
94
45258
0.281634
0.076731
13
0.30
111
51750
0.299868
0.087738
14
0.30
107
52262
0.310858
0.088606
15
0.30
114
55438
0.300087
0.093991
16
0.65
42
15348
0.083961
0.026021
17
0.30
111
51676
0.304704
0.087613
18
0.30
109
51414
0.293492
0.087168
19
0.30
107
52034
0.298169
0.088220
In [159]:
irisStatsPD.to_csv(path_or_buf=folder + name + "_stats.csv")
In [160]:
irisStatsPD['dataset']=name
In [73]:
allStatsPD = pd.read_csv("/home/chiroptera/QCThesis/experiments/threshold/all_stats.csv")
In [76]:
allStatsPD.groupby("dataset").describe()
Out[76]:
density
max_assocs
n_assocs
n_assocs_pc
threshold
dataset
house_votes
count
20.000000
20.000000
20.000000
20.000000
20.000000
mean
0.082077
37.200000
4417.700000
0.161542
0.542500
std
0.103824
34.330591
5588.227448
0.202918
0.157509
min
0.019620
12.000000
1056.000000
0.039094
0.000000
25%
0.042397
24.000000
2282.000000
0.083453
0.500000
50%
0.068538
32.500000
3689.000000
0.134770
0.525000
75%
0.079416
36.500000
4274.500000
0.156169
0.612500
max
0.511408
176.000000
27526.000000
1.000000
0.750000
ionosphere
count
20.000000
20.000000
20.000000
20.000000
20.000000
mean
0.006342
7.000000
781.300000
0.011596
0.882500
std
0.015413
13.603405
1898.900516
0.028146
0.311733
min
0.000000
0.000000
0.000000
0.000000
0.000000
25%
0.003072
3.000000
378.500000
0.005603
1.000000
50%
0.003157
4.000000
389.000000
0.005851
1.000000
75%
0.003279
5.000000
404.000000
0.005972
1.000000
max
0.071680
64.000000
8831.000000
0.130909
1.000000
iris
count
20.000000
20.000000
20.000000
20.000000
20.000000
mean
0.108733
24.950000
2446.500000
0.241998
0.632500
std
0.014686
3.425523
330.428032
0.039123
0.049404
min
0.073778
18.000000
1660.000000
0.159173
0.600000
25%
0.105756
23.750000
2379.500000
0.227508
0.600000
50%
0.114667
25.500000
2580.000000
0.259635
0.600000
75%
0.118956
27.000000
2676.500000
0.268773
0.650000
max
0.121333
33.000000
2730.000000
0.284069
0.750000
pima
count
20.000000
20.000000
20.000000
20.000000
20.000000
mean
0.076742
97.000000
45264.100000
0.261772
0.365000
std
0.023573
25.663818
13904.044447
0.082531
0.132883
min
0.019436
35.000000
11464.000000
0.064200
0.250000
25%
0.076785
97.750000
45289.500000
0.264608
0.300000
50%
0.087209
107.500000
51438.000000
0.295831
0.300000
75%
0.088568
111.000000
52239.500000
0.305272
0.350000
max
0.095652
116.000000
56418.000000
0.342787
0.700000
synthetic_control
count
20.000000
20.000000
20.000000
20.000000
20.000000
mean
0.059183
100.000000
21305.900000
0.196395
0.647500
std
0.003841
0.000000
1382.834690
0.019524
0.034317
min
0.050272
100.000000
18098.000000
0.164606
0.600000
25%
0.058492
100.000000
21057.000000
0.179398
0.637500
50%
0.059756
100.000000
21512.000000
0.200540
0.650000
75%
0.061304
100.000000
22069.500000
0.207242
0.650000
max
0.064361
100.000000
23170.000000
0.232961
0.700000
wine
count
20.000000
20.000000
20.000000
20.000000
20.000000
mean
0.075388
20.900000
2388.600000
0.176541
0.722500
std
0.009091
2.468752
288.029311
0.023238
0.037958
min
0.059967
17.000000
1900.000000
0.143830
0.650000
25%
0.068331
19.000000
2165.000000
0.160566
0.700000
50%
0.074201
20.000000
2351.000000
0.171510
0.725000
75%
0.082202
22.000000
2604.500000
0.191682
0.750000
max
0.093296
27.000000
2956.000000
0.224928
0.800000
In [24]:
raw = allStatsPD.values
In [68]:
allStatsPD = pd.DataFrame(raw, columns=columns)
In [112]:
print desc.to_html()
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>density</th>
<th>max_assocs</th>
<th>n_assocs</th>
<th>n_assocs_pc</th>
<th>threshold</th>
</tr>
<tr>
<th>dataset</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="8" valign="top">house_votes</th>
<th>count</th>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
</tr>
<tr>
<th>mean</th>
<td>0.082077</td>
<td>37.200000</td>
<td>4417.700000</td>
<td>0.161542</td>
<td>0.542500</td>
</tr>
<tr>
<th>std</th>
<td>0.103824</td>
<td>34.330591</td>
<td>5588.227448</td>
<td>0.202918</td>
<td>0.157509</td>
</tr>
<tr>
<th>min</th>
<td>0.019620</td>
<td>12.000000</td>
<td>1056.000000</td>
<td>0.039094</td>
<td>0.000000</td>
</tr>
<tr>
<th>25%</th>
<td>0.042397</td>
<td>24.000000</td>
<td>2282.000000</td>
<td>0.083453</td>
<td>0.500000</td>
</tr>
<tr>
<th>50%</th>
<td>0.068538</td>
<td>32.500000</td>
<td>3689.000000</td>
<td>0.134770</td>
<td>0.525000</td>
</tr>
<tr>
<th>75%</th>
<td>0.079416</td>
<td>36.500000</td>
<td>4274.500000</td>
<td>0.156169</td>
<td>0.612500</td>
</tr>
<tr>
<th>max</th>
<td>0.511408</td>
<td>176.000000</td>
<td>27526.000000</td>
<td>1.000000</td>
<td>0.750000</td>
</tr>
<tr>
<th rowspan="8" valign="top">ionosphere</th>
<th>count</th>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
</tr>
<tr>
<th>mean</th>
<td>0.006342</td>
<td>7.000000</td>
<td>781.300000</td>
<td>0.011596</td>
<td>0.882500</td>
</tr>
<tr>
<th>std</th>
<td>0.015413</td>
<td>13.603405</td>
<td>1898.900516</td>
<td>0.028146</td>
<td>0.311733</td>
</tr>
<tr>
<th>min</th>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
<td>0.000000</td>
</tr>
<tr>
<th>25%</th>
<td>0.003072</td>
<td>3.000000</td>
<td>378.500000</td>
<td>0.005603</td>
<td>1.000000</td>
</tr>
<tr>
<th>50%</th>
<td>0.003157</td>
<td>4.000000</td>
<td>389.000000</td>
<td>0.005851</td>
<td>1.000000</td>
</tr>
<tr>
<th>75%</th>
<td>0.003279</td>
<td>5.000000</td>
<td>404.000000</td>
<td>0.005972</td>
<td>1.000000</td>
</tr>
<tr>
<th>max</th>
<td>0.071680</td>
<td>64.000000</td>
<td>8831.000000</td>
<td>0.130909</td>
<td>1.000000</td>
</tr>
<tr>
<th rowspan="8" valign="top">iris</th>
<th>count</th>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
</tr>
<tr>
<th>mean</th>
<td>0.108733</td>
<td>24.950000</td>
<td>2446.500000</td>
<td>0.241998</td>
<td>0.632500</td>
</tr>
<tr>
<th>std</th>
<td>0.014686</td>
<td>3.425523</td>
<td>330.428032</td>
<td>0.039123</td>
<td>0.049404</td>
</tr>
<tr>
<th>min</th>
<td>0.073778</td>
<td>18.000000</td>
<td>1660.000000</td>
<td>0.159173</td>
<td>0.600000</td>
</tr>
<tr>
<th>25%</th>
<td>0.105756</td>
<td>23.750000</td>
<td>2379.500000</td>
<td>0.227508</td>
<td>0.600000</td>
</tr>
<tr>
<th>50%</th>
<td>0.114667</td>
<td>25.500000</td>
<td>2580.000000</td>
<td>0.259635</td>
<td>0.600000</td>
</tr>
<tr>
<th>75%</th>
<td>0.118956</td>
<td>27.000000</td>
<td>2676.500000</td>
<td>0.268773</td>
<td>0.650000</td>
</tr>
<tr>
<th>max</th>
<td>0.121333</td>
<td>33.000000</td>
<td>2730.000000</td>
<td>0.284069</td>
<td>0.750000</td>
</tr>
<tr>
<th rowspan="8" valign="top">pima</th>
<th>count</th>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
</tr>
<tr>
<th>mean</th>
<td>0.076742</td>
<td>97.000000</td>
<td>45264.100000</td>
<td>0.261772</td>
<td>0.365000</td>
</tr>
<tr>
<th>std</th>
<td>0.023573</td>
<td>25.663818</td>
<td>13904.044447</td>
<td>0.082531</td>
<td>0.132883</td>
</tr>
<tr>
<th>min</th>
<td>0.019436</td>
<td>35.000000</td>
<td>11464.000000</td>
<td>0.064200</td>
<td>0.250000</td>
</tr>
<tr>
<th>25%</th>
<td>0.076785</td>
<td>97.750000</td>
<td>45289.500000</td>
<td>0.264608</td>
<td>0.300000</td>
</tr>
<tr>
<th>50%</th>
<td>0.087209</td>
<td>107.500000</td>
<td>51438.000000</td>
<td>0.295831</td>
<td>0.300000</td>
</tr>
<tr>
<th>75%</th>
<td>0.088568</td>
<td>111.000000</td>
<td>52239.500000</td>
<td>0.305272</td>
<td>0.350000</td>
</tr>
<tr>
<th>max</th>
<td>0.095652</td>
<td>116.000000</td>
<td>56418.000000</td>
<td>0.342787</td>
<td>0.700000</td>
</tr>
<tr>
<th rowspan="8" valign="top">synthetic_control</th>
<th>count</th>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
</tr>
<tr>
<th>mean</th>
<td>0.059183</td>
<td>100.000000</td>
<td>21305.900000</td>
<td>0.196395</td>
<td>0.647500</td>
</tr>
<tr>
<th>std</th>
<td>0.003841</td>
<td>0.000000</td>
<td>1382.834690</td>
<td>0.019524</td>
<td>0.034317</td>
</tr>
<tr>
<th>min</th>
<td>0.050272</td>
<td>100.000000</td>
<td>18098.000000</td>
<td>0.164606</td>
<td>0.600000</td>
</tr>
<tr>
<th>25%</th>
<td>0.058492</td>
<td>100.000000</td>
<td>21057.000000</td>
<td>0.179398</td>
<td>0.637500</td>
</tr>
<tr>
<th>50%</th>
<td>0.059756</td>
<td>100.000000</td>
<td>21512.000000</td>
<td>0.200540</td>
<td>0.650000</td>
</tr>
<tr>
<th>75%</th>
<td>0.061304</td>
<td>100.000000</td>
<td>22069.500000</td>
<td>0.207242</td>
<td>0.650000</td>
</tr>
<tr>
<th>max</th>
<td>0.064361</td>
<td>100.000000</td>
<td>23170.000000</td>
<td>0.232961</td>
<td>0.700000</td>
</tr>
<tr>
<th rowspan="8" valign="top">wine</th>
<th>count</th>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
<td>20.000000</td>
</tr>
<tr>
<th>mean</th>
<td>0.075388</td>
<td>20.900000</td>
<td>2388.600000</td>
<td>0.176541</td>
<td>0.722500</td>
</tr>
<tr>
<th>std</th>
<td>0.009091</td>
<td>2.468752</td>
<td>288.029311</td>
<td>0.023238</td>
<td>0.037958</td>
</tr>
<tr>
<th>min</th>
<td>0.059967</td>
<td>17.000000</td>
<td>1900.000000</td>
<td>0.143830</td>
<td>0.650000</td>
</tr>
<tr>
<th>25%</th>
<td>0.068331</td>
<td>19.000000</td>
<td>2165.000000</td>
<td>0.160566</td>
<td>0.700000</td>
</tr>
<tr>
<th>50%</th>
<td>0.074201</td>
<td>20.000000</td>
<td>2351.000000</td>
<td>0.171510</td>
<td>0.725000</td>
</tr>
<tr>
<th>75%</th>
<td>0.082202</td>
<td>22.000000</td>
<td>2604.500000</td>
<td>0.191682</td>
<td>0.750000</td>
</tr>
<tr>
<th>max</th>
<td>0.093296</td>
<td>27.000000</td>
<td>2956.000000</td>
<td>0.224928</td>
<td>0.800000</td>
</tr>
</tbody>
</table>
In [109]:
print desc.to_html
\begin{tabular}{llrrrrr}
\toprule
& & density & max\_assocs & n\_assocs & n\_assocs\_pc & threshold \\
\midrule
house\_votes & count & & & & & \\
ionosphere & mean & 20.000000 & 20.000000 & 20.000000 & 20.000000 & 20.000000 \\
iris & std & 0.082077 & 37.200000 & 4417.700000 & 0.161542 & 0.542500 \\
pima & min & 0.103824 & 34.330591 & 5588.227448 & 0.202918 & 0.157509 \\
synthetic\_control & 25\% & 0.019620 & 12.000000 & 1056.000000 & 0.039094 & 0.000000 \\
wine & 50\% & 0.042397 & 24.000000 & 2282.000000 & 0.083453 & 0.500000 \\
\bottomrule
\end{tabular}
In [ ]:
In [91]:
desc_d=desc.to_dense()
In [80]:
desc=allStatsPD.groupby("dataset").describe()
In [14]:
allStatsPD.dataset.unique()
Out[14]:
array(['iris', 'wine', 'house_votes', 'ionosphere', 'synthetic_control',
'pima'], dtype=object)
In [105]:
#allStatsPD = allStatsPD.append(irisStatsPD,ignore_index=True)
allStatsPD.to_csv(folder + "all_stats.csv", index=False)
allStatsPD
Out[105]:
threshold
max_assocs
n_assocs
n_assocs_pc
density
dataset
0
0.60
26
2558
0.263983
0.113689
iris
1
0.65
24
2466
0.237481
0.109600
iris
2
0.60
26
2692
0.269308
0.119644
iris
3
0.60
26
2672
0.227404
0.118756
iris
4
0.65
25
2406
0.253050
0.106933
iris
5
0.75
18
1668
0.166135
0.074133
iris
6
0.65
22
2288
0.194459
0.101689
iris
7
0.60
24
2690
0.271169
0.119556
iris
8
0.60
29
2664
0.284069
0.118400
iris
9
0.60
27
2654
0.263294
0.117956
iris
10
0.60
27
2720
0.263107
0.120889
iris
11
0.75
19
1660
0.170782
0.073778
iris
12
0.60
26
2622
0.272331
0.116533
iris
13
0.60
33
2694
0.268594
0.119733
iris
14
0.70
21
1940
0.159173
0.086222
iris
15
0.65
23
2300
0.227543
0.102222
iris
16
0.60
24
2452
0.247227
0.108978
iris
17
0.65
24
2452
0.256164
0.108978
iris
18
0.60
27
2730
0.264484
0.121333
iris
19
0.60
28
2602
0.280207
0.115644
iris
20
0.80
17
1900
0.143830
0.059967
wine
21
0.70
22
2446
0.158010
0.077200
wine
22
0.75
19
2096
0.147897
0.066153
wine
23
0.75
20
2256
0.174020
0.071203
wine
24
0.75
19
2174
0.169658
0.068615
wine
25
0.70
20
2464
0.172452
0.077768
wine
26
0.75
19
2122
0.161418
0.066974
wine
27
0.65
25
2956
0.224928
0.093296
wine
28
0.70
22
2632
0.174721
0.083070
wine
29
0.75
19
2066
0.167831
0.065206
wine
...
...
...
...
...
...
...
90
0.65
100
21566
0.201393
0.059906
synthetic_control
91
0.60
100
23170
0.228830
0.064361
synthetic_control
92
0.70
100
19428
0.172393
0.053967
synthetic_control
93
0.65
100
20946
0.205914
0.058183
synthetic_control
94
0.65
100
21796
0.179524
0.060544
synthetic_control
95
0.65
100
21280
0.192450
0.059111
synthetic_control
96
0.60
100
22632
0.218815
0.062867
synthetic_control
97
0.65
100
21586
0.209854
0.059961
synthetic_control
98
0.65
100
21094
0.201140
0.058594
synthetic_control
99
0.70
100
18098
0.164614
0.050272
synthetic_control
100
0.30
110
52232
0.312504
0.088555
pima
101
0.70
35
11464
0.064200
0.019436
pima
102
0.30
115
54418
0.321912
0.092261
pima
103
0.35
100
45452
0.268804
0.077060
pima
104
0.30
116
54126
0.288269
0.091766
pima
105
0.35
90
45018
0.268992
0.076324
pima
106
0.30
108
51462
0.306976
0.087250
pima
107
0.30
114
50788
0.300407
0.086107
pima
108
0.25
111
56418
0.342787
0.095652
pima
109
0.35
105
48216
0.251723
0.081746
pima
110
0.35
99
45300
0.252022
0.076803
pima
111
0.65
42
15208
0.084069
0.025784
pima
112
0.35
94
45258
0.281634
0.076731
pima
113
0.30
111
51750
0.299868
0.087738
pima
114
0.30
107
52262
0.310858
0.088606
pima
115
0.30
114
55438
0.300087
0.093991
pima
116
0.65
42
15348
0.083961
0.026021
pima
117
0.30
111
51676
0.304704
0.087613
pima
118
0.30
109
51414
0.293492
0.087168
pima
119
0.30
107
52034
0.298169
0.088220
pima
120 rows × 6 columns
Content source: Chiroptera/QCThesis
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