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  1. README.md +28 -1
  2. australian.dat +690 -0
  3. australian_credit.py +91 -0
README.md CHANGED
@@ -1,3 +1,30 @@
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  ---
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- license: cc-by-4.0
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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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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+
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+
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+ # Usage
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+ ```python
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+ from datasets import load_dataset
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+
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+ dataset = load_dataset("mstz/australian_credit", "australian_credit")["train"]
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+ ```
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577
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578
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579
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580
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581
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583
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584
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595
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598
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601
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606
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614
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615
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617
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618
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619
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620
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621
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622
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
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634
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635
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636
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652
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653
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655
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656
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657
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658
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659
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
+ 0 25.75 0.5 2 8 8 0.875 1 0 0 1 2 491 1 1
676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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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
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+ 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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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