First commit

README.md

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+---
+language:
+- {en}  # Example: fr
+tags:
+- {adult}  # Example: audio
+- {tabular_classification}  # Example: bio
+- {binary_classification}  # Example: bio
+pretty_name: {Adult}  # Example: SQuAD
+size_categories:
+- {10K<n<100K}  # Example: n<1K, 100K<n<1M, …
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- {tabular}
+configs:  # Optional for datasets with multiple configurations like glue.
+- {income}  # Example for glue: sst2
+- {income-no race}  # Example for glue: cola
+- {race}  # Example for glue: cola

adult.py

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+"""Diva: A Fraud Detection Dataset"""
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+__ORIGINAL_FEATURE_NAMES = [
+    "age",
+    "workclass",
+    "final_weight",
+    "education", "education-num",
+    "marital_status",
+    "occupation",
+    "relationship",
+    "race",
+    "sex",
+    "capital_gain",
+    "capital_loss",
+    "hours_per_week",
+    "native_country",
+    "threshold"
+]
+__BASE_FEATURE_NAMES = [
+    "age",
+    "capital_gain",
+    "capital_loss",
+    "education",
+    "final_weight",
+    "hours_per_week",
+    "marital_status",
+    "native_country",
+    "occupation",
+    "race",
+    "relationship",
+    "sex",
+    "workclass",
+    "threshold",
+]
+__DESCRIPTION = "Adult dataset from the UCI ML repository."
+__HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Adult"
+__URLS = ("https://huggingface.co/datasets/mstz/adult/raw/adult.csv")
+__CITATION = """
+@inproceedings{DBLP:conf/kdd/Kohavi96,
+  author    = {Ron Kohavi},
+  editor    = {Evangelos Simoudis and
+               Jiawei Han and
+               Usama M. Fayyad},
+  title     = {Scaling Up the Accuracy of Naive-Bayes Classifiers: {A} Decision-Tree
+               Hybrid},
+  booktitle = {Proceedings of the Second International Conference on Knowledge Discovery
+               and Data Mining (KDD-96), Portland, Oregon, {USA}},
+  pages     = {202--207},
+  publisher = {{AAAI} Press},
+  year      = {1996},
+  url       = {http://www.aaai.org/Library/KDD/1996/kdd96-033.php},
+  timestamp = {Mon, 05 Jun 2017 13:20:21 +0200},
+  biburl    = {https://dblp.org/rec/conf/kdd/Kohavi96.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}"""
+
+# Dataset info
+__urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/adult/raw/adult_tr.csv", 
+    "test": "https://huggingface.co/datasets/mstz/adult/raw/adult_ts.csv"
+}
+__features_per_config = {
+    "income": datasets.Features({"age": datasets.Value("int8"),
+                                 "capital_gain": datasets.Value("float16"),
+                                 "capital_loss": datasets.Value("float16"),
+                                 "education": datasets.Value("int8"),
+                                 "final_weight": datasets.Value("int16"),
+                                 "hours_per_week": datasets.Value("int16"),
+                                 "marital_status": datasets.Value("string"),
+                                 "native_country": datasets.Value("string"),
+                                 "occupation": datasets.Value("string"),
+                                 "race": datasets.Value("string"),
+                                 "relationship": datasets.Value("string"),
+                                 "sex": datasets.Value("binary"),
+                                 "workclass": datasets.Value("binary"),
+                                 "threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
+                                }),
+    "income-no race": datasets.Features({"age": datasets.Value("int8"),
+                                         "capital_gain": datasets.Value("float16"),
+                                         "capital_loss": datasets.Value("float16"),
+                                         "education": datasets.Value("int8"),
+                                         "final_weight": datasets.Value("int16"),
+                                         "hours_per_week": datasets.Value("int16"),
+                                         "marital_status": datasets.Value("string"),
+                                         "native_country": datasets.Value("string"),
+                                         "occupation": datasets.Value("string"),
+                                         "relationship": datasets.Value("string"),
+                                         "sex": datasets.Value("binary"),
+                                         "workclass": datasets.Value("binary"),
+                                         "threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
+                                        }),
+    "race": datasets.Features({"age": datasets.Value("int8"),
+                               "capital_gain": datasets.Value("float16"),
+                               "capital_loss": datasets.Value("float16"),
+                               "education": datasets.Value("int8"),
+                               "final_weight": datasets.Value("int16"),
+                               "hours_per_week": datasets.Value("int16"),
+                               "marital_status": datasets.Value("string"),
+                               "native_country": datasets.Value("string"),
+                               "occupation": datasets.Value("string"),
+                               "relationship": datasets.Value("string"),
+                               "sex": datasets.Value("binary"),
+                               "workclass": datasets.Value("binary"),
+                               "over_threshold": datasets.Value("binary"),
+                               "race": datasets.ClassLabel(num_classes=5, names=["White",
+                                                                                "Black",
+                                                                                "Asian-Pac-Islander",
+                                                                                "Amer-Indian-Eskimo",
+                                                                                "Other"]),
+                              }),
+}
+
+
+class AdultConfig(datasets.BuilderConfig):
+    def __init__(self, features: datasets.Features, labels_names: datasets.ClassLabel, **kwargs):
+        super(AdultConfig, self).__init__(version = VERSION, **kwargs)
+        self.features = features
+        self.labels.names = labels_names
+
+
+class Adult(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "income"
+    BUILDER_CONFIGS = [
+        datasets.AdultConfig(name="income", version=VERSION,
+                             description="Adult for income threshold binary classification."),
+        datasets.AdultConfig(name="income-no race", version=VERSION,
+                             description="Adult for income threshold binary classification, race excluded from features."),
+        datasets.AdultConfig(name="race", version=VERSION,
+                             description="Adult for race multiclass classification."),
+    ]
+
+    def _info(self):
+        if self.config_name not in __features_per_config:
+            raise ValueError(f"Unknown configuration: {self.config_name}")
+        
+        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"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"]}),
+        ]
+    
+    def _generate_examples(self, filepath: str):
+        data = pandas.read_csv(filepath)
+        data = self.preprocess(data, config=self.config_name)
+
+        for row in data.iterrows():
+            data_row = dict(row)
+            row_id = hash(str(data_row))
+
+            yield row_id, data_row
+
+    def preprocess(self, data: pandas.DataFrame, config: str = "income") -> pandas.DataFrame:
+        data.drop(["education"], inplace=True)
+        data = data[["age", "capital_gain", "capital_loss", "education", "final_weight",
+                     "hours_per_week", "marital_status", "native_country", "occupation",
+                     "race", "relationship", "sex", "workclass", "threshold"]]
+        data.columns = __BASE_FEATURE_NAMES
+        
+        return data
+    
+    def income_preprocessing(data: pandas.DataFrame) -> pandas.DataFrame:
+        data = data[__features_per_config["income"]]
+
+        return data
+        
+    def income_norace_preprocessing(data: pandas.DataFrame) -> pandas.DataFrame:
+        data = data[__features_per_config["income-no race"]]
+
+        return data
+    
+    def race_preprocessing(data: pandas.DataFrame) -> pandas.DataFrame:
+        data["over_threshold"] = df.threshold
+        data = data[__features_per_config["race"]]
+
+        return data
+
+    # TODO: add custom split?