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README.md
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---
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-
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---
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---
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language:
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- en
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tags:
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- ozone
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- tabular_classification
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- binary_classification
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pretty_name: Ozone
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size_categories:
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- 1K<n<10K
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task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
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- tabular-classification
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configs:
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- 8hr
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- 1hr
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---
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# Ozone
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The [Ozone dataset](https://archive.ics.uci.edu/ml/datasets/Ozone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
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# Configurations and tasks
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| **Configuration** | **Task** | **Description** |
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|-------------------|---------------------------|-------------------------|
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| 8hr | Binary classification | Is there an ozone layer?|
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| 1hr | Binary classification | Is there an ozone layer?|
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# Usage
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```python
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from datasets import load_dataset
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dataset = load_dataset("mstz/ozone", "8hr")["train"]
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```
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pima.csv
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1 |
+
number_of_pregnancies,plasma_glucose_concentration,diastolic_blood_pressure,triceps_thickness,serum_insulin,bmi,diabetes_pedigree,age,class
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2 |
+
6,148,72,35,0,33.6,0.627,50,1
|
3 |
+
1,85,66,29,0,26.6,0.351,31,0
|
4 |
+
8,183,64,0,0,23.3,0.672,32,1
|
5 |
+
1,89,66,23,94,28.1,0.167,21,0
|
6 |
+
0,137,40,35,168,43.1,2.288,33,1
|
7 |
+
5,116,74,0,0,25.6,0.201,30,0
|
8 |
+
3,78,50,32,88,31,0.248,26,1
|
9 |
+
10,115,0,0,0,35.3,0.134,29,0
|
10 |
+
2,197,70,45,543,30.5,0.158,53,1
|
11 |
+
8,125,96,0,0,0,0.232,54,1
|
12 |
+
4,110,92,0,0,37.6,0.191,30,0
|
13 |
+
10,168,74,0,0,38,0.537,34,1
|
14 |
+
10,139,80,0,0,27.1,1.441,57,0
|
15 |
+
1,189,60,23,846,30.1,0.398,59,1
|
16 |
+
5,166,72,19,175,25.8,0.587,51,1
|
17 |
+
7,100,0,0,0,30,0.484,32,1
|
18 |
+
0,118,84,47,230,45.8,0.551,31,1
|
19 |
+
7,107,74,0,0,29.6,0.254,31,1
|
20 |
+
1,103,30,38,83,43.3,0.183,33,0
|
21 |
+
1,115,70,30,96,34.6,0.529,32,1
|
22 |
+
3,126,88,41,235,39.3,0.704,27,0
|
23 |
+
8,99,84,0,0,35.4,0.388,50,0
|
24 |
+
7,196,90,0,0,39.8,0.451,41,1
|
25 |
+
9,119,80,35,0,29,0.263,29,1
|
26 |
+
11,143,94,33,146,36.6,0.254,51,1
|
27 |
+
10,125,70,26,115,31.1,0.205,41,1
|
28 |
+
7,147,76,0,0,39.4,0.257,43,1
|
29 |
+
1,97,66,15,140,23.2,0.487,22,0
|
30 |
+
13,145,82,19,110,22.2,0.245,57,0
|
31 |
+
5,117,92,0,0,34.1,0.337,38,0
|
32 |
+
5,109,75,26,0,36,0.546,60,0
|
33 |
+
3,158,76,36,245,31.6,0.851,28,1
|
34 |
+
3,88,58,11,54,24.8,0.267,22,0
|
35 |
+
6,92,92,0,0,19.9,0.188,28,0
|
36 |
+
10,122,78,31,0,27.6,0.512,45,0
|
37 |
+
4,103,60,33,192,24,0.966,33,0
|
38 |
+
11,138,76,0,0,33.2,0.42,35,0
|
39 |
+
9,102,76,37,0,32.9,0.665,46,1
|
40 |
+
2,90,68,42,0,38.2,0.503,27,1
|
41 |
+
4,111,72,47,207,37.1,1.39,56,1
|
42 |
+
3,180,64,25,70,34,0.271,26,0
|
43 |
+
7,133,84,0,0,40.2,0.696,37,0
|
44 |
+
7,106,92,18,0,22.7,0.235,48,0
|
45 |
+
9,171,110,24,240,45.4,0.721,54,1
|
46 |
+
7,159,64,0,0,27.4,0.294,40,0
|
47 |
+
0,180,66,39,0,42,1.893,25,1
|
48 |
+
1,146,56,0,0,29.7,0.564,29,0
|
49 |
+
2,71,70,27,0,28,0.586,22,0
|
50 |
+
7,103,66,32,0,39.1,0.344,31,1
|
51 |
+
7,105,0,0,0,0,0.305,24,0
|
52 |
+
1,103,80,11,82,19.4,0.491,22,0
|
53 |
+
1,101,50,15,36,24.2,0.526,26,0
|
54 |
+
5,88,66,21,23,24.4,0.342,30,0
|
55 |
+
8,176,90,34,300,33.7,0.467,58,1
|
56 |
+
7,150,66,42,342,34.7,0.718,42,0
|
57 |
+
1,73,50,10,0,23,0.248,21,0
|
58 |
+
7,187,68,39,304,37.7,0.254,41,1
|
59 |
+
0,100,88,60,110,46.8,0.962,31,0
|
60 |
+
0,146,82,0,0,40.5,1.781,44,0
|
61 |
+
0,105,64,41,142,41.5,0.173,22,0
|
62 |
+
2,84,0,0,0,0,0.304,21,0
|
63 |
+
8,133,72,0,0,32.9,0.27,39,1
|
64 |
+
5,44,62,0,0,25,0.587,36,0
|
65 |
+
2,141,58,34,128,25.4,0.699,24,0
|
66 |
+
7,114,66,0,0,32.8,0.258,42,1
|
67 |
+
5,99,74,27,0,29,0.203,32,0
|
68 |
+
0,109,88,30,0,32.5,0.855,38,1
|
69 |
+
2,109,92,0,0,42.7,0.845,54,0
|
70 |
+
1,95,66,13,38,19.6,0.334,25,0
|
71 |
+
4,146,85,27,100,28.9,0.189,27,0
|
72 |
+
2,100,66,20,90,32.9,0.867,28,1
|
73 |
+
5,139,64,35,140,28.6,0.411,26,0
|
74 |
+
13,126,90,0,0,43.4,0.583,42,1
|
75 |
+
4,129,86,20,270,35.1,0.231,23,0
|
76 |
+
1,79,75,30,0,32,0.396,22,0
|
77 |
+
1,0,48,20,0,24.7,0.14,22,0
|
78 |
+
7,62,78,0,0,32.6,0.391,41,0
|
79 |
+
5,95,72,33,0,37.7,0.37,27,0
|
80 |
+
0,131,0,0,0,43.2,0.27,26,1
|
81 |
+
2,112,66,22,0,25,0.307,24,0
|
82 |
+
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309 |
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310 |
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311 |
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312 |
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313 |
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314 |
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318 |
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319 |
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320 |
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321 |
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322 |
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323 |
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324 |
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325 |
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326 |
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327 |
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328 |
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329 |
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10,179,70,0,0,35.1,0.2,37,0
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330 |
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2,102,86,36,120,45.5,0.127,23,1
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577 |
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578 |
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2,118,80,0,0,42.9,0.693,21,1
|
580 |
+
10,133,68,0,0,27,0.245,36,0
|
581 |
+
2,197,70,99,0,34.7,0.575,62,1
|
582 |
+
0,151,90,46,0,42.1,0.371,21,1
|
583 |
+
6,109,60,27,0,25,0.206,27,0
|
584 |
+
12,121,78,17,0,26.5,0.259,62,0
|
585 |
+
8,100,76,0,0,38.7,0.19,42,0
|
586 |
+
8,124,76,24,600,28.7,0.687,52,1
|
587 |
+
1,93,56,11,0,22.5,0.417,22,0
|
588 |
+
8,143,66,0,0,34.9,0.129,41,1
|
589 |
+
6,103,66,0,0,24.3,0.249,29,0
|
590 |
+
3,176,86,27,156,33.3,1.154,52,1
|
591 |
+
0,73,0,0,0,21.1,0.342,25,0
|
592 |
+
11,111,84,40,0,46.8,0.925,45,1
|
593 |
+
2,112,78,50,140,39.4,0.175,24,0
|
594 |
+
3,132,80,0,0,34.4,0.402,44,1
|
595 |
+
2,82,52,22,115,28.5,1.699,25,0
|
596 |
+
6,123,72,45,230,33.6,0.733,34,0
|
597 |
+
0,188,82,14,185,32,0.682,22,1
|
598 |
+
0,67,76,0,0,45.3,0.194,46,0
|
599 |
+
1,89,24,19,25,27.8,0.559,21,0
|
600 |
+
1,173,74,0,0,36.8,0.088,38,1
|
601 |
+
1,109,38,18,120,23.1,0.407,26,0
|
602 |
+
1,108,88,19,0,27.1,0.4,24,0
|
603 |
+
6,96,0,0,0,23.7,0.19,28,0
|
604 |
+
1,124,74,36,0,27.8,0.1,30,0
|
605 |
+
7,150,78,29,126,35.2,0.692,54,1
|
606 |
+
4,183,0,0,0,28.4,0.212,36,1
|
607 |
+
1,124,60,32,0,35.8,0.514,21,0
|
608 |
+
1,181,78,42,293,40,1.258,22,1
|
609 |
+
1,92,62,25,41,19.5,0.482,25,0
|
610 |
+
0,152,82,39,272,41.5,0.27,27,0
|
611 |
+
1,111,62,13,182,24,0.138,23,0
|
612 |
+
3,106,54,21,158,30.9,0.292,24,0
|
613 |
+
3,174,58,22,194,32.9,0.593,36,1
|
614 |
+
7,168,88,42,321,38.2,0.787,40,1
|
615 |
+
6,105,80,28,0,32.5,0.878,26,0
|
616 |
+
11,138,74,26,144,36.1,0.557,50,1
|
617 |
+
3,106,72,0,0,25.8,0.207,27,0
|
618 |
+
6,117,96,0,0,28.7,0.157,30,0
|
619 |
+
2,68,62,13,15,20.1,0.257,23,0
|
620 |
+
9,112,82,24,0,28.2,1.282,50,1
|
621 |
+
0,119,0,0,0,32.4,0.141,24,1
|
622 |
+
2,112,86,42,160,38.4,0.246,28,0
|
623 |
+
2,92,76,20,0,24.2,1.698,28,0
|
624 |
+
6,183,94,0,0,40.8,1.461,45,0
|
625 |
+
0,94,70,27,115,43.5,0.347,21,0
|
626 |
+
2,108,64,0,0,30.8,0.158,21,0
|
627 |
+
4,90,88,47,54,37.7,0.362,29,0
|
628 |
+
0,125,68,0,0,24.7,0.206,21,0
|
629 |
+
0,132,78,0,0,32.4,0.393,21,0
|
630 |
+
5,128,80,0,0,34.6,0.144,45,0
|
631 |
+
4,94,65,22,0,24.7,0.148,21,0
|
632 |
+
7,114,64,0,0,27.4,0.732,34,1
|
633 |
+
0,102,78,40,90,34.5,0.238,24,0
|
634 |
+
2,111,60,0,0,26.2,0.343,23,0
|
635 |
+
1,128,82,17,183,27.5,0.115,22,0
|
636 |
+
10,92,62,0,0,25.9,0.167,31,0
|
637 |
+
13,104,72,0,0,31.2,0.465,38,1
|
638 |
+
5,104,74,0,0,28.8,0.153,48,0
|
639 |
+
2,94,76,18,66,31.6,0.649,23,0
|
640 |
+
7,97,76,32,91,40.9,0.871,32,1
|
641 |
+
1,100,74,12,46,19.5,0.149,28,0
|
642 |
+
0,102,86,17,105,29.3,0.695,27,0
|
643 |
+
4,128,70,0,0,34.3,0.303,24,0
|
644 |
+
6,147,80,0,0,29.5,0.178,50,1
|
645 |
+
4,90,0,0,0,28,0.61,31,0
|
646 |
+
3,103,72,30,152,27.6,0.73,27,0
|
647 |
+
2,157,74,35,440,39.4,0.134,30,0
|
648 |
+
1,167,74,17,144,23.4,0.447,33,1
|
649 |
+
0,179,50,36,159,37.8,0.455,22,1
|
650 |
+
11,136,84,35,130,28.3,0.26,42,1
|
651 |
+
0,107,60,25,0,26.4,0.133,23,0
|
652 |
+
1,91,54,25,100,25.2,0.234,23,0
|
653 |
+
1,117,60,23,106,33.8,0.466,27,0
|
654 |
+
5,123,74,40,77,34.1,0.269,28,0
|
655 |
+
2,120,54,0,0,26.8,0.455,27,0
|
656 |
+
1,106,70,28,135,34.2,0.142,22,0
|
657 |
+
2,155,52,27,540,38.7,0.24,25,1
|
658 |
+
2,101,58,35,90,21.8,0.155,22,0
|
659 |
+
1,120,80,48,200,38.9,1.162,41,0
|
660 |
+
11,127,106,0,0,39,0.19,51,0
|
661 |
+
3,80,82,31,70,34.2,1.292,27,1
|
662 |
+
10,162,84,0,0,27.7,0.182,54,0
|
663 |
+
1,199,76,43,0,42.9,1.394,22,1
|
664 |
+
8,167,106,46,231,37.6,0.165,43,1
|
665 |
+
9,145,80,46,130,37.9,0.637,40,1
|
666 |
+
6,115,60,39,0,33.7,0.245,40,1
|
667 |
+
1,112,80,45,132,34.8,0.217,24,0
|
668 |
+
4,145,82,18,0,32.5,0.235,70,1
|
669 |
+
10,111,70,27,0,27.5,0.141,40,1
|
670 |
+
6,98,58,33,190,34,0.43,43,0
|
671 |
+
9,154,78,30,100,30.9,0.164,45,0
|
672 |
+
6,165,68,26,168,33.6,0.631,49,0
|
673 |
+
1,99,58,10,0,25.4,0.551,21,0
|
674 |
+
10,68,106,23,49,35.5,0.285,47,0
|
675 |
+
3,123,100,35,240,57.3,0.88,22,0
|
676 |
+
8,91,82,0,0,35.6,0.587,68,0
|
677 |
+
6,195,70,0,0,30.9,0.328,31,1
|
678 |
+
9,156,86,0,0,24.8,0.23,53,1
|
679 |
+
0,93,60,0,0,35.3,0.263,25,0
|
680 |
+
3,121,52,0,0,36,0.127,25,1
|
681 |
+
2,101,58,17,265,24.2,0.614,23,0
|
682 |
+
2,56,56,28,45,24.2,0.332,22,0
|
683 |
+
0,162,76,36,0,49.6,0.364,26,1
|
684 |
+
0,95,64,39,105,44.6,0.366,22,0
|
685 |
+
4,125,80,0,0,32.3,0.536,27,1
|
686 |
+
5,136,82,0,0,0,0.64,69,0
|
687 |
+
2,129,74,26,205,33.2,0.591,25,0
|
688 |
+
3,130,64,0,0,23.1,0.314,22,0
|
689 |
+
1,107,50,19,0,28.3,0.181,29,0
|
690 |
+
1,140,74,26,180,24.1,0.828,23,0
|
691 |
+
1,144,82,46,180,46.1,0.335,46,1
|
692 |
+
8,107,80,0,0,24.6,0.856,34,0
|
693 |
+
13,158,114,0,0,42.3,0.257,44,1
|
694 |
+
2,121,70,32,95,39.1,0.886,23,0
|
695 |
+
7,129,68,49,125,38.5,0.439,43,1
|
696 |
+
2,90,60,0,0,23.5,0.191,25,0
|
697 |
+
7,142,90,24,480,30.4,0.128,43,1
|
698 |
+
3,169,74,19,125,29.9,0.268,31,1
|
699 |
+
0,99,0,0,0,25,0.253,22,0
|
700 |
+
4,127,88,11,155,34.5,0.598,28,0
|
701 |
+
4,118,70,0,0,44.5,0.904,26,0
|
702 |
+
2,122,76,27,200,35.9,0.483,26,0
|
703 |
+
6,125,78,31,0,27.6,0.565,49,1
|
704 |
+
1,168,88,29,0,35,0.905,52,1
|
705 |
+
2,129,0,0,0,38.5,0.304,41,0
|
706 |
+
4,110,76,20,100,28.4,0.118,27,0
|
707 |
+
6,80,80,36,0,39.8,0.177,28,0
|
708 |
+
10,115,0,0,0,0,0.261,30,1
|
709 |
+
2,127,46,21,335,34.4,0.176,22,0
|
710 |
+
9,164,78,0,0,32.8,0.148,45,1
|
711 |
+
2,93,64,32,160,38,0.674,23,1
|
712 |
+
3,158,64,13,387,31.2,0.295,24,0
|
713 |
+
5,126,78,27,22,29.6,0.439,40,0
|
714 |
+
10,129,62,36,0,41.2,0.441,38,1
|
715 |
+
0,134,58,20,291,26.4,0.352,21,0
|
716 |
+
3,102,74,0,0,29.5,0.121,32,0
|
717 |
+
7,187,50,33,392,33.9,0.826,34,1
|
718 |
+
3,173,78,39,185,33.8,0.97,31,1
|
719 |
+
10,94,72,18,0,23.1,0.595,56,0
|
720 |
+
1,108,60,46,178,35.5,0.415,24,0
|
721 |
+
5,97,76,27,0,35.6,0.378,52,1
|
722 |
+
4,83,86,19,0,29.3,0.317,34,0
|
723 |
+
1,114,66,36,200,38.1,0.289,21,0
|
724 |
+
1,149,68,29,127,29.3,0.349,42,1
|
725 |
+
5,117,86,30,105,39.1,0.251,42,0
|
726 |
+
1,111,94,0,0,32.8,0.265,45,0
|
727 |
+
4,112,78,40,0,39.4,0.236,38,0
|
728 |
+
1,116,78,29,180,36.1,0.496,25,0
|
729 |
+
0,141,84,26,0,32.4,0.433,22,0
|
730 |
+
2,175,88,0,0,22.9,0.326,22,0
|
731 |
+
2,92,52,0,0,30.1,0.141,22,0
|
732 |
+
3,130,78,23,79,28.4,0.323,34,1
|
733 |
+
8,120,86,0,0,28.4,0.259,22,1
|
734 |
+
2,174,88,37,120,44.5,0.646,24,1
|
735 |
+
2,106,56,27,165,29,0.426,22,0
|
736 |
+
2,105,75,0,0,23.3,0.56,53,0
|
737 |
+
4,95,60,32,0,35.4,0.284,28,0
|
738 |
+
0,126,86,27,120,27.4,0.515,21,0
|
739 |
+
8,65,72,23,0,32,0.6,42,0
|
740 |
+
2,99,60,17,160,36.6,0.453,21,0
|
741 |
+
1,102,74,0,0,39.5,0.293,42,1
|
742 |
+
11,120,80,37,150,42.3,0.785,48,1
|
743 |
+
3,102,44,20,94,30.8,0.4,26,0
|
744 |
+
1,109,58,18,116,28.5,0.219,22,0
|
745 |
+
9,140,94,0,0,32.7,0.734,45,1
|
746 |
+
13,153,88,37,140,40.6,1.174,39,0
|
747 |
+
12,100,84,33,105,30,0.488,46,0
|
748 |
+
1,147,94,41,0,49.3,0.358,27,1
|
749 |
+
1,81,74,41,57,46.3,1.096,32,0
|
750 |
+
3,187,70,22,200,36.4,0.408,36,1
|
751 |
+
6,162,62,0,0,24.3,0.178,50,1
|
752 |
+
4,136,70,0,0,31.2,1.182,22,1
|
753 |
+
1,121,78,39,74,39,0.261,28,0
|
754 |
+
3,108,62,24,0,26,0.223,25,0
|
755 |
+
0,181,88,44,510,43.3,0.222,26,1
|
756 |
+
8,154,78,32,0,32.4,0.443,45,1
|
757 |
+
1,128,88,39,110,36.5,1.057,37,1
|
758 |
+
7,137,90,41,0,32,0.391,39,0
|
759 |
+
0,123,72,0,0,36.3,0.258,52,1
|
760 |
+
1,106,76,0,0,37.5,0.197,26,0
|
761 |
+
6,190,92,0,0,35.5,0.278,66,1
|
762 |
+
2,88,58,26,16,28.4,0.766,22,0
|
763 |
+
9,170,74,31,0,44,0.403,43,1
|
764 |
+
9,89,62,0,0,22.5,0.142,33,0
|
765 |
+
10,101,76,48,180,32.9,0.171,63,0
|
766 |
+
2,122,70,27,0,36.8,0.34,27,0
|
767 |
+
5,121,72,23,112,26.2,0.245,30,0
|
768 |
+
1,126,60,0,0,30.1,0.349,47,1
|
769 |
+
1,93,70,31,0,30.4,0.315,23,0
|
pima.py
ADDED
@@ -0,0 +1,73 @@
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
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|
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|
|
|
1 |
+
"""Pima"""
|
2 |
+
|
3 |
+
from typing import List
|
4 |
+
|
5 |
+
import datasets
|
6 |
+
|
7 |
+
import pandas
|
8 |
+
|
9 |
+
|
10 |
+
VERSION = datasets.Version("1.0.0")
|
11 |
+
|
12 |
+
|
13 |
+
DESCRIPTION = "Pima dataset from the OpenML repository."
|
14 |
+
_HOMEPAGE = "https://www.openml.org/search?type=data&status=active&id=37"
|
15 |
+
_URLS = ("https://www.openml.org/search?type=data&status=active&id=37")
|
16 |
+
_CITATION = """"""
|
17 |
+
|
18 |
+
# Dataset info
|
19 |
+
urls_per_split = {
|
20 |
+
"train": "https://huggingface.co/datasets/mstz/pima/raw/main/pima.csv"
|
21 |
+
}
|
22 |
+
features_types_per_config = {
|
23 |
+
"pima": {
|
24 |
+
"number_of_pregnancies": datasets.Value("int8"),
|
25 |
+
"plasma_glucose_concentration": datasets.Value("float64"),
|
26 |
+
"diastolic_blood_pressure": datasets.Value("float64"),
|
27 |
+
"triceps_thickness": datasets.Value("float64"),
|
28 |
+
"serum_insulin": datasets.Value("float64"),
|
29 |
+
"bmi": datasets.Value("float64"),
|
30 |
+
"diabetes_pedigree": datasets.Value("float64"),
|
31 |
+
"age": datasets.Value("float64"),
|
32 |
+
"class": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
|
33 |
+
},
|
34 |
+
|
35 |
+
}
|
36 |
+
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
|
37 |
+
|
38 |
+
|
39 |
+
class PimaConfig(datasets.BuilderConfig):
|
40 |
+
def __init__(self, **kwargs):
|
41 |
+
super(PimaConfig, self).__init__(version=VERSION, **kwargs)
|
42 |
+
self.features = features_per_config[kwargs["name"]]
|
43 |
+
|
44 |
+
|
45 |
+
class Pima(datasets.GeneratorBasedBuilder):
|
46 |
+
# dataset versions
|
47 |
+
DEFAULT_CONFIG = "pima"
|
48 |
+
BUILDER_CONFIGS = [
|
49 |
+
PimaConfig(name="pima",
|
50 |
+
description="Pima for binary classification."),
|
51 |
+
]
|
52 |
+
|
53 |
+
|
54 |
+
def _info(self):
|
55 |
+
info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
|
56 |
+
features=features_per_config[self.config.name])
|
57 |
+
|
58 |
+
return info
|
59 |
+
|
60 |
+
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
|
61 |
+
downloads = dl_manager.download_and_extract(urls_per_split)
|
62 |
+
|
63 |
+
return [
|
64 |
+
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name]["train"]})
|
65 |
+
]
|
66 |
+
|
67 |
+
def _generate_examples(self, filepath: str):
|
68 |
+
data = pandas.read_csv(filepath)
|
69 |
+
|
70 |
+
for row_id, row in data.iterrows():
|
71 |
+
data_row = dict(row)
|
72 |
+
|
73 |
+
yield row_id, data_row
|