Update parquet files

README.md

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----
-language:
-- en
-tags:
-- adult
-- tabular_classification
-- binary_classification
-- multiclass_classification
-pretty_name: Adult
-size_categories:
-- 10K<n<100K
-task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
-- tabular-classification
-configs:
-- encoding
-- income
-- income-no race
-- race
----
-# Adult
-The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
-Census dataset including personal characteristic of a person, and their income threshold.
-
-# Configurations and tasks
-| **Configuration** | **Task**                  | Description                                                   |
-|-------------------|---------------------------|---------------------------------------------------------------|
-| encoding          |                           | Encoding dictionary                                           |
-| income            | Binary classification     | Classify the person's income as over or under the threshold.  |
-| income-no race    | Binary classification     | As `income`, but the `race` feature is removed.               |
-| race              | Multiclass classification | Predict the race of the individual.                           |
-
-# Usage
-```
-from datasets import load_dataset
-from sklearn.tree import DecisionTreeClassifier
-
-dataset = load_dataset("mstz/adult", "income")["train"]
-```
-
-# Features
-|**Feature**        |**Type**   | **Description**                                           |
-|-------------------|-----------|-----------------------------------------------------------|
-|`age`              |`[int64]`  | Age of the person                                         |
-|`capital_gain`     |`[float64]`| Capital gained by the person                              |
-|`capital_loss`     |`[float64]`| Capital lost by the person                                |
-|`education`        |`[int8]`   | Education level: the higher, the more educated the person |
-|`final_weight`     |`[int64]`  |                                                           |
-|`hours_per_week`   |`[int64]`  | Hours worked per week                                     |
-|`marital_status`   |`[string]` | Marital status of the person                              |
-|`native_country`   |`[string]` | Native country of the person                              |
-|`occupation`       |`[string]` | Job of the person                                         |
-|`race`             |`[string]` | Race of the person                                        |
-|`relationship`     |`[string]` |                                                           |
-|`sex`              |`[int8]`   | Sex of the person                                         |
-|`workclass`        |`[string]` | Type of job of the person                                 |
-|`over_threshold`   |`int8`     |`1` for income `>= 50k$`, `0` otherwise                    |

acute_inflammation.py

@@ -1,103 +0,0 @@
-"""Acute_Inflammation"""
-
-from typing import List
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-_BASE_FEATURE_NAMES = [
-    "temperature",
-    "has_nausea",
-    "has_lumbar_pain",
-    "has_urine_pushing",
-    "has_micturition_pains",
-    "has_burnt_urethra",
-    "has_inflammed_bladder",
-    "has_nephritis_of_renal_pelvis",
-    "has_acute_inflammation"
-]
-
-DESCRIPTION = "Acute_Inflammation dataset from the UCI ML repository."
-_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Acute_Inflammation"
-_URLS = ("https://huggingface.co/datasets/mstz/acute_inflammation/raw/main/diagnosis.csv")
-_CITATION = """
-@misc{misc_acute_inflammations_184,
-  author       = {Czerniak,Jacek},
-  title        = {{Acute Inflammations}},
-  year         = {2009},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C5V59S}}
-}"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/acute_inflammation/raw/main/diagnosis.csv"
-}
-features_types_per_config = {
-    "inflammation": {
-        "temperature": datasets.Value("float64"),
-        "has_nausea": datasets.Value("bool"),
-        "has_lumbar_pain": datasets.Value("bool"),
-        "has_urine_pushing": datasets.Value("bool"),
-        "has_micturition_pains": datasets.Value("bool"),
-        "has_burnt_urethra": datasets.Value("bool"),
-        "has_inflammed_bladder": datasets.Value("bool"),
-        "has_nephritis_of_renal_pelvis": datasets.Value("bool"),
-        "has_acute_inflammation": datasets.ClassLabel(num_classes=2)
-    }
-}
-features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
-
-
-class Acute_InflammationConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(Acute_InflammationConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Acute_Inflammation(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "inflammation"
-    BUILDER_CONFIGS = [
-        Acute_InflammationConfig(name="inflammation",
-                    description="Binary classification of inflammation.")
-    ]
-
-
-    def _info(self):
-        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
-                                    features=features_per_config[self.config.name])
-
-        return info
-    
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        downloads = dl_manager.download_and_extract(urls_per_split)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
-        ]
-    
-    def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath, header=None)
-        data = self.preprocess(data, config=self.config.name)
-
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
-
-            yield row_id, data_row
-
-    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
-        data.columns = _BASE_FEATURE_NAMES
-        boolean_features = ["has_nausea", "has_lumbar_pain", "has_urine_pushing",
-                            "has_micturition_pains", "has_burnt_urethra", "has_inflammed_bladder",
-                            "has_nephritis_of_renal_pelvis", "has_acute_inflammation"]
-        for f in boolean_features:
-            data.loc[:, f] = data[f].apply(lambda x: True if x == "yes" else False)
-
-        data = data.astype({f: "bool" for f in boolean_features})
-        data = data.astype({"has_acute_inflammation": int})
-        
-        return data

diagnosis.csv

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inflammation/acute_inflammation-train.parquet

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