Datasets:
Upload 3 files
Browse files- README.md +28 -1
- australian.dat +690 -0
- australian_credit.py +91 -0
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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- australian_credit
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- tabular_classification
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- binary_classification
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pretty_name: Australian Credit
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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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- australian_credit
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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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| australian_credit | Binary classification | Is the loan granted?|
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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/australian_credit", "australian_credit")["train"]
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```
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australian.dat
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1 |
+
1 22.08 11.46 2 4 4 1.585 0 0 0 1 2 100 1213 0
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0 22.67 7 2 8 4 0.165 0 0 0 0 2 160 1 0
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0 29.58 1.75 1 4 4 1.25 0 0 0 1 2 280 1 0
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4 |
+
0 21.67 11.5 1 5 3 0 1 1 11 1 2 0 1 1
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5 |
+
1 20.17 8.17 2 6 4 1.96 1 1 14 0 2 60 159 1
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6 |
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0 15.83 0.585 2 8 8 1.5 1 1 2 0 2 100 1 1
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7 |
+
1 17.42 6.5 2 3 4 0.125 0 0 0 0 2 60 101 0
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8 |
+
0 58.67 4.46 2 11 8 3.04 1 1 6 0 2 43 561 1
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+
1 27.83 1 1 2 8 3 0 0 0 0 2 176 538 0
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10 |
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0 55.75 7.08 2 4 8 6.75 1 1 3 1 2 100 51 0
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11 |
+
1 33.5 1.75 2 14 8 4.5 1 1 4 1 2 253 858 1
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12 |
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1 41.42 5 2 11 8 5 1 1 6 1 2 470 1 1
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13 |
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1 20.67 1.25 1 8 8 1.375 1 1 3 1 2 140 211 0
|
14 |
+
1 34.92 5 2 14 8 7.5 1 1 6 1 2 0 1001 1
|
15 |
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1 58.58 2.71 2 8 4 2.415 0 0 0 1 2 320 1 0
|
16 |
+
1 48.08 6.04 2 4 4 0.04 0 0 0 0 2 0 2691 1
|
17 |
+
1 29.58 4.5 2 9 4 7.5 1 1 2 1 2 330 1 1
|
18 |
+
0 18.92 9 2 6 4 0.75 1 1 2 0 2 88 592 1
|
19 |
+
1 20 1.25 1 4 4 0.125 0 0 0 0 2 140 5 0
|
20 |
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0 22.42 5.665 2 11 4 2.585 1 1 7 0 2 129 3258 1
|
21 |
+
0 28.17 0.585 2 6 4 0.04 0 0 0 0 2 260 1005 0
|
22 |
+
0 19.17 0.585 1 6 4 0.585 1 0 0 1 2 160 1 0
|
23 |
+
1 41.17 1.335 2 2 4 0.165 0 0 0 0 2 168 1 0
|
24 |
+
1 41.58 1.75 2 4 4 0.21 1 0 0 0 2 160 1 0
|
25 |
+
1 19.5 9.585 2 6 4 0.79 0 0 0 0 2 80 351 0
|
26 |
+
1 32.75 1.5 2 13 8 5.5 1 1 3 1 2 0 1 1
|
27 |
+
1 22.5 0.125 1 4 4 0.125 0 0 0 0 2 200 71 0
|
28 |
+
1 33.17 3.04 1 8 8 2.04 1 1 1 1 2 180 18028 1
|
29 |
+
0 30.67 12 2 8 4 2 1 1 1 0 2 220 20 1
|
30 |
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|
31 |
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|
32 |
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|
33 |
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|
34 |
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1 23.08 11.5 2 9 8 2.125 1 1 11 1 2 290 285 1
|
35 |
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1 42.83 1.25 2 7 4 13.875 0 1 1 1 2 352 113 0
|
36 |
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1 74.83 19 1 1 1 0.04 0 1 2 0 2 0 352 0
|
37 |
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1 25 12.5 2 6 4 3 1 0 0 1 1 20 1 1
|
38 |
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39 |
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0 47.75 8 2 8 4 7.875 1 1 6 1 2 0 1261 1
|
40 |
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0 47.42 3 2 14 4 13.875 1 1 2 1 2 519 1705 1
|
41 |
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1 23.17 0 2 13 4 0.08500 1 0 0 0 2 0 1 1
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42 |
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|
43 |
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1 26.75 1.125 2 14 8 1.25 1 0 0 0 2 0 5299 1
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44 |
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1 63.33 0.54 2 8 4 0.585 1 1 3 1 2 180 1 0
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45 |
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46 |
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|
47 |
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48 |
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1 16.17 0.04 2 8 4 0.04 0 0 0 0 2 0 1 1
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49 |
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0 29.5 2 1 10 8 2 0 0 0 0 2 256 18 0
|
50 |
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|
51 |
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52 |
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53 |
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54 |
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|
55 |
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|
56 |
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|
57 |
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58 |
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|
59 |
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|
60 |
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61 |
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62 |
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63 |
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64 |
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|
65 |
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66 |
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1 42 9.79 2 14 8 7.96 1 1 8 0 2 0 1 1
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67 |
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0 19.5 0.165 2 11 4 0.04 0 0 0 1 2 380 1 0
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68 |
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1 21.5 11.5 2 3 4 0.5 1 0 0 1 2 100 69 0
|
69 |
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|
70 |
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71 |
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72 |
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73 |
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74 |
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75 |
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76 |
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77 |
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79 |
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1 23.08 0 2 4 4 1 0 1 11 0 1 0 1 0
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80 |
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|
81 |
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82 |
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83 |
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0 18.58 10 2 2 4 0.415 0 0 0 0 2 80 43 0
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84 |
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|
85 |
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86 |
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1 37.58 0 2 8 4 0 0 0 0 0 3 184 1 1
|
87 |
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|
88 |
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0 47 13 2 3 5 5.165 1 1 9 1 2 0 1 1
|
89 |
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|
90 |
+
1 22.17 0.585 1 1 1 0 0 0 0 0 2 100 1 0
|
91 |
+
1 27.75 1.29 2 4 8 0.25 0 0 0 1 1 140 1 0
|
92 |
+
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|
93 |
+
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|
94 |
+
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|
95 |
+
0 18.17 10 1 11 8 0.165 0 0 0 0 2 340 1 0
|
96 |
+
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|
97 |
+
0 38.92 1.665 2 6 4 0.25 0 0 0 0 2 0 391 0
|
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|
608 |
+
1 39.17 1.625 2 8 4 1.5 1 1 10 0 2 186 4701 1
|
609 |
+
0 27.67 2.04 2 9 4 0.25 0 0 0 1 2 180 51 0
|
610 |
+
1 19.17 9.5 2 9 4 1.5 1 0 0 0 2 120 2207 1
|
611 |
+
1 39.5 4.25 2 8 5 6.5 1 1 16 0 2 117 1211 1
|
612 |
+
1 36.42 0.75 1 2 4 0.585 0 0 0 0 2 240 4 0
|
613 |
+
1 26.17 0.25 2 3 5 0 1 0 0 1 2 0 1 1
|
614 |
+
1 39.42 1.71 1 7 4 0.165 0 0 0 0 1 400 1 0
|
615 |
+
1 41.92 0.42 2 8 8 0.21 1 1 6 0 2 220 949 1
|
616 |
+
1 21.33 7.5 2 6 4 1.415 1 1 1 0 2 80 9801 1
|
617 |
+
0 20.08 1.25 2 8 4 0 0 0 0 0 2 0 1 0
|
618 |
+
0 27.58 3 2 7 4 2.79 0 1 1 1 2 280 11 0
|
619 |
+
1 21.58 0.79 1 13 4 0.665 0 0 0 0 2 160 1 0
|
620 |
+
1 28.25 5.125 2 14 4 4.75 1 1 2 0 2 420 8 1
|
621 |
+
1 26.92 2.25 2 3 5 0.5 0 0 0 1 2 640 4001 0
|
622 |
+
0 18.42 9.25 2 11 4 1.21 1 1 4 0 2 60 541 1
|
623 |
+
0 23.25 5.875 2 11 4 3.17 1 1 10 0 2 120 246 1
|
624 |
+
1 23.58 1.79 2 8 4 0.54 0 0 0 1 2 136 2 0
|
625 |
+
1 28.33 5 2 9 4 11 1 0 0 1 2 70 1 1
|
626 |
+
1 25.67 2.21 1 6 4 4 1 0 0 0 2 188 1 0
|
627 |
+
0 28.58 1.665 2 11 4 2.415 1 0 0 1 2 440 1 0
|
628 |
+
1 22.92 3.165 1 8 4 0.165 0 0 0 0 2 160 1059 0
|
629 |
+
0 24.5 1.04 1 1 1 0.5 1 1 3 0 2 180 148 0
|
630 |
+
1 24.75 0.54 2 7 4 1 0 0 0 1 2 120 2 0
|
631 |
+
1 32 1.75 1 10 8 0.04 1 0 0 1 2 393 1 1
|
632 |
+
1 48.08 3.75 2 3 5 1 0 0 0 0 2 100 3 0
|
633 |
+
0 28.42 3.5 2 9 4 0.835 1 0 0 0 1 280 1 1
|
634 |
+
1 25.42 0.54 2 9 4 0.165 0 1 1 0 2 272 445 0
|
635 |
+
1 18.58 10.29 2 1 1 0.415 0 0 0 0 2 80 1 0
|
636 |
+
1 21.67 1.165 1 4 4 2.5 1 1 1 0 2 180 21 0
|
637 |
+
1 24.58 1.25 2 8 4 0.25 0 0 0 0 2 110 1 0
|
638 |
+
1 16.33 4.085 2 3 8 0.415 0 0 0 1 2 120 1 0
|
639 |
+
1 26.75 2 2 2 4 0.75 0 0 0 1 2 80 1 0
|
640 |
+
1 48.17 3.5 2 6 4 3.5 1 0 0 0 1 230 1 1
|
641 |
+
1 23.5 2.75 2 1 1 4.5 0 0 0 0 2 160 26 0
|
642 |
+
0 41 2.04 1 11 8 0.125 1 1 23 1 2 455 1237 1
|
643 |
+
1 35.17 25.125 2 14 8 1.625 1 1 1 1 2 515 501 1
|
644 |
+
1 23.75 12 2 8 4 2.085 0 0 0 0 1 80 1 0
|
645 |
+
1 18.67 5 2 11 4 0.375 1 1 2 0 2 0 39 0
|
646 |
+
1 30.33 0.5 2 2 8 0.08500 0 0 0 1 1 252 1 0
|
647 |
+
0 19.75 0.75 2 8 4 0.795 1 1 5 1 2 140 6 0
|
648 |
+
1 26.17 0.835 2 13 4 1.165 0 0 0 0 2 100 1 0
|
649 |
+
1 28.75 1.5 1 8 4 1.5 1 0 0 1 2 0 226 0
|
650 |
+
1 28.67 9.335 2 11 8 5.665 1 1 6 0 2 381 169 1
|
651 |
+
1 26 1 2 11 4 1.75 1 0 0 1 2 280 1 1
|
652 |
+
1 23.5 3.165 1 4 4 0.415 0 1 1 1 2 280 81 0
|
653 |
+
1 26.33 13 2 10 2 0 0 0 0 1 2 140 1111 0
|
654 |
+
1 54.33 6.75 2 8 8 2.625 1 1 11 1 2 0 285 1
|
655 |
+
1 31.57 0.375 2 2 4 0.875 1 0 0 1 1 928 1 0
|
656 |
+
0 25.25 12.5 2 2 4 1 0 0 0 1 2 180 1063 0
|
657 |
+
1 33.17 3.165 1 14 4 3.165 1 1 3 1 2 380 1 1
|
658 |
+
1 39.5 1.625 2 8 4 1.5 0 0 0 0 2 0 317 0
|
659 |
+
0 17.67 0 1 5 1 0 0 0 0 0 2 86 1 0
|
660 |
+
1 23.92 0.665 2 8 4 0.165 0 0 0 0 2 100 1 1
|
661 |
+
1 26.67 2.71 1 13 4 5.25 1 1 1 0 2 211 1 1
|
662 |
+
0 22.5 0.415 2 3 4 0.335 0 0 0 1 1 144 1 0
|
663 |
+
1 39.92 0.54 1 6 4 0.5 1 1 3 0 2 200 1001 1
|
664 |
+
0 26.08 8.665 2 6 4 1.415 1 0 0 0 2 160 151 1
|
665 |
+
1 20 0 2 2 4 0.5 0 0 0 0 2 144 1 0
|
666 |
+
1 31.57 4 1 3 4 0.08500 0 0 0 1 2 411 1 0
|
667 |
+
1 26.75 4.5 1 8 5 2.5 0 0 0 0 2 200 1211 0
|
668 |
+
0 24.92 1.25 2 1 1 0 1 0 0 0 2 80 1 0
|
669 |
+
0 32.25 1.5 2 8 4 0.25 0 0 0 1 2 372 123 0
|
670 |
+
1 17.67 4.46 2 8 4 0.25 0 0 0 0 1 80 1 0
|
671 |
+
0 37.75 5.5 2 11 4 0.125 1 0 0 1 2 228 1 1
|
672 |
+
1 22.67 2.54 1 8 8 2.585 1 0 0 0 2 0 1 1
|
673 |
+
0 17.92 10.21 2 1 1 0 0 0 0 0 2 0 51 0
|
674 |
+
1 24.42 12.335 2 11 8 1.585 1 0 0 1 2 120 1 1
|
675 |
+
0 25.75 0.5 2 8 8 0.875 1 0 0 1 2 491 1 1
|
676 |
+
1 26.17 12.5 1 4 8 1.25 0 0 0 1 2 0 18 0
|
677 |
+
0 22.75 6.165 2 6 4 0.165 0 0 0 0 2 220 1001 0
|
678 |
+
1 23 0.75 2 7 4 0.5 1 0 0 1 1 320 1 0
|
679 |
+
1 25.67 0.29 1 8 4 1.5 0 0 0 1 2 160 1 0
|
680 |
+
1 48.58 0.205 1 4 4 0.25 1 1 11 0 2 380 2733 1
|
681 |
+
1 21.17 0 2 8 4 0.5 0 0 0 1 1 0 1 0
|
682 |
+
1 35.25 16.5 1 8 4 4 1 0 0 0 2 80 1 0
|
683 |
+
0 22.92 11.585 2 13 4 0.04 1 0 0 0 2 80 1350 1
|
684 |
+
0 48.17 1.335 2 3 7 0.335 0 0 0 0 2 0 121 0
|
685 |
+
1 43 0.29 1 13 8 1.75 1 1 8 0 2 100 376 1
|
686 |
+
1 31.57 10.5 2 14 4 6.5 1 0 0 0 2 0 1 1
|
687 |
+
1 20.67 0.415 2 8 4 0.125 0 0 0 0 2 0 45 0
|
688 |
+
0 18.83 9.54 2 6 4 0.08500 1 0 0 0 2 100 1 1
|
689 |
+
0 27.42 14.5 2 14 8 3.085 1 1 1 0 2 120 12 1
|
690 |
+
1 41 0.04 2 10 4 0.04 0 1 1 0 1 560 1 1
|
australian_credit.py
ADDED
@@ -0,0 +1,91 @@
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|
1 |
+
"""Australian_credit"""
|
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 |
+
DESCRIPTION = "Australian_credit dataset from the UCI ML repository."
|
13 |
+
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Australian_credit"
|
14 |
+
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Australian_credit")
|
15 |
+
_CITATION = """
|
16 |
+
@misc{misc_statlog_(australian_credit_approval)_143,
|
17 |
+
author = {Quinlan,Ross},
|
18 |
+
title = {{Statlog (Australian Credit Approval)}},
|
19 |
+
howpublished = {UCI Machine Learning Repository},
|
20 |
+
note = {{DOI}: \\url{10.24432/C59012}}
|
21 |
+
}"""
|
22 |
+
|
23 |
+
# Dataset info
|
24 |
+
urls_per_split = {
|
25 |
+
"train": "https://huggingface.co/datasets/mstz/australian_credit/raw/main/SPECT.train"
|
26 |
+
}
|
27 |
+
features_types_per_config = {
|
28 |
+
"australian_credit": {
|
29 |
+
"feature_1": datasets.Value("bool"),
|
30 |
+
"feature_2": datasets.Value("float64"),
|
31 |
+
"feature_3": datasets.Value("float64"),
|
32 |
+
"feature_4": datasets.Value("string"),
|
33 |
+
"feature_5": datasets.Value("string"),
|
34 |
+
"feature_6": datasets.Value("string"),
|
35 |
+
"feature_7": datasets.Value("float64"),
|
36 |
+
"feature_8": datasets.Value("string"),
|
37 |
+
"feature_9": datasets.Value("string"),
|
38 |
+
"feature_10": datasets.Value("float64"),
|
39 |
+
"feature_11": datasets.Value("string"),
|
40 |
+
"feature_12": datasets.Value("string"),
|
41 |
+
"feature_13": datasets.Value("float64"),
|
42 |
+
"feature_14": datasets.Value("float64"),
|
43 |
+
"is_granted": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
|
44 |
+
}
|
45 |
+
|
46 |
+
}
|
47 |
+
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
|
48 |
+
|
49 |
+
|
50 |
+
class Australian_creditConfig(datasets.BuilderConfig):
|
51 |
+
def __init__(self, **kwargs):
|
52 |
+
super(Australian_creditConfig, self).__init__(version=VERSION, **kwargs)
|
53 |
+
self.features = features_per_config[kwargs["name"]]
|
54 |
+
|
55 |
+
|
56 |
+
class Australian_credit(datasets.GeneratorBasedBuilder):
|
57 |
+
# dataset versions
|
58 |
+
DEFAULT_CONFIG = "australian_credit"
|
59 |
+
BUILDER_CONFIGS = [
|
60 |
+
Australian_creditConfig(name="australian_credit",
|
61 |
+
description="Australian_credit for binary classification.")
|
62 |
+
]
|
63 |
+
|
64 |
+
|
65 |
+
def _info(self):
|
66 |
+
info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
|
67 |
+
features=features_per_config[self.config.name])
|
68 |
+
|
69 |
+
return info
|
70 |
+
|
71 |
+
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
|
72 |
+
downloads = dl_manager.download_and_extract(urls_per_split)
|
73 |
+
|
74 |
+
return [
|
75 |
+
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name]["train"]})
|
76 |
+
]
|
77 |
+
|
78 |
+
def _generate_examples(self, filepath: str):
|
79 |
+
data = pandas.read_csv(filepath, header=None)
|
80 |
+
data = self.preprocess(data)
|
81 |
+
|
82 |
+
for row_id, row in data.iterrows():
|
83 |
+
data_row = dict(row)
|
84 |
+
|
85 |
+
yield row_id, data_row
|
86 |
+
|
87 |
+
def preprocess(self, data):
|
88 |
+
features = list(features_types_per_config[self.config.name])
|
89 |
+
data.features = features
|
90 |
+
|
91 |
+
return data
|