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README.md

@@ -1,3 +1,56 @@
 ---
-license: cc
+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

@@ -0,0 +1,99 @@
+"""Acute_Inflamamtion"""
+
+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"
+]
+
+DESCRIPTION = "Acute_Inflamamtion dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Acute_Inflamamtion"
+_URLS = ("https://huggingface.co/datasets/mstz/acute_inflamamtion/raw/diagnosis.data")
+_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_inflamamtion/raw/main/diagnosis.data"
+}
+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("int8")
+    }
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class Acute_InflamamtionConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(Acute_InflamamtionConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Acute_Inflamamtion(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "inflammation"
+    BUILDER_CONFIGS = [
+        Acute_InflamamtionConfig(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)
+        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"]
+        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})
+        
+        return data