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Datasets:
mstz
/
planning

Tasks:
Tabular Classification
Modalities:
Tabular
Languages:
English
Size:
< 1K
Tags:
planning
tabular_classification
binary_classification
UCI
Libraries:
Datasets
License:
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  1. README.md +28 -1
  2. planning.py +97 -0
  3. plrx.csv +183 -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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+ - planning
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+ - tabular_classification
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+ - binary_classification
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+ pretty_name: Planning
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+ size_categories:
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+ - 100<n<1K
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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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+ - planning
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  ---
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+ # Planning
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+ The [Planning dataset](https://archive.ics.uci.edu/ml/datasets/Planning) 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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+ | planning | Binary classification | Is the patient in a planning state?|
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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/planning", "planning")["train"]
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+ ```
planning.py ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Planning: A Census Dataset"""
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+
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+ from typing import List
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+
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+ import datasets
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+
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+ import pandas
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+
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+
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+ VERSION = datasets.Version("1.0.0")
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+ _BASE_FEATURE_NAMES = [
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+ "V1",
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+ "V2",
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+ "V3",
15
+ "V4",
16
+ "V5",
17
+ "V6",
18
+ "V7",
19
+ "V8",
20
+ "V9",
21
+ "V10",
22
+ "V11",
23
+ "V12",
24
+ "class"
25
+ ]
26
+
27
+ DESCRIPTION = "Planning dataset from the UCI ML repository."
28
+ _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Planning"
29
+ _URLS = ("https://archive.ics.uci.edu/ml/datasets/Planning")
30
+ _CITATION = """
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+ @misc{misc_planning_relax_230,
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+ author = {Bhatt,Rajen},
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+ title = {{Planning Relax}},
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+ year = {2012},
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+ howpublished = {UCI Machine Learning Repository},
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+ note = {{DOI}: \\url{10.24432/C5T023}}
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+ }"""
38
+
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+ # Dataset info
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+ urls_per_split = {
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+ "planning": {"train": "https://huggingface.co/datasets/mstz/planning/raw/main/plrx.csv"}
42
+ }
43
+ features_types_per_config = {
44
+ "planning": {
45
+ "V1": datasets.Value("float64"),
46
+ "V2": datasets.Value("float64"),
47
+ "V3": datasets.Value("float64"),
48
+ "V4": datasets.Value("float64"),
49
+ "V5": datasets.Value("float64"),
50
+ "V6": datasets.Value("float64"),
51
+ "V7": datasets.Value("float64"),
52
+ "V8": datasets.Value("float64"),
53
+ "V9": datasets.Value("float64"),
54
+ "V10": datasets.Value("float64"),
55
+ "V11": datasets.Value("float64"),
56
+ "V12": datasets.Value("float64"),
57
+ "class": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
58
+ }
59
+
60
+ }
61
+ features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
62
+
63
+
64
+ classPlanningConfig(datasets.BuilderConfig):
65
+ def __init__(self, **kwargs):
66
+ super(PlanningConfig, self).__init__(version=VERSION, **kwargs)
67
+ self.features = features_per_config[kwargs["name"]]
68
+
69
+
70
+ classPlanning(datasets.GeneratorBasedBuilder):
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+ # dataset versions
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+ DEFAULT_CONFIG = "planning"
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+ BUILDER_CONFIGS = [
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+ PlanningConfig(name="planning",
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+ description="Planning for binary classification.")
76
+ ]
77
+
78
+ def _info(self):
79
+ info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
80
+ features=features_per_config[self.config.name])
81
+
82
+ return info
83
+
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+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
85
+ downloads = dl_manager.download_and_extract(urls_per_split)
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name]["train"]})
89
+ ]
90
+
91
+ def _generate_examples(self, filepath: str):
92
+ data = pandas.read_csv(filepath)
93
+
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+ for row_id, row in data.iterrows():
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+ data_row = dict(row)
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+
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+ yield row_id, data_row
plrx.csv ADDED
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