updated to datasets 4.*

README.md

@@ -1,23 +1,36 @@
 ---
-language:
-- en
+configs:
+- config_name: income
+  data_files:
+  - path: income/train.csv
+    split: train
+  - path: income/test.csv
+    split: test
+  default: true
+- config_name: income-no race
+  data_files:
+  - path: income-no race/train.csv
+    split: train
+  - path: income-no race/test.csv
+    split: test
+  default: false
+- config_name: race
+  data_files:
+  - path: race/train.csv
+    split: train
+  - path: race/test.csv
+    split: test
+  default: false
+language: en
+license: cc
+pretty_name: Adult
+size_categories: 1M<n<10M
 tags:
-- adult
 - tabular_classification
 - binary_classification
 - multiclass_classification
-- UCI
-pretty_name: Adult
-size_categories:
-- 10K<n<100K
 task_categories:
 - tabular-classification
-configs:
-- encoding
-- income
-- income-no race
-- race
-license: cc
 ---
 # Adult
 The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
@@ -26,7 +39,6 @@ Census dataset including personal characteristic of a person, and their income t
 # Configurations and tasks
 | **Configuration** | **Task**                  | Description                                                     |
 |-------------------|---------------------------|-----------------------------------------------------------------|
-| encoding          |                           | Encoding dictionary showing original values of encoded features.|
 | 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.                             |

adult.py

@@ -1,250 +0,0 @@
-from typing import List
-from functools import partial
-
-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_worked_per_week",
-    "marital_status",
-    "native_country",
-    "occupation",
-    "race",
-    "relationship",
-    "is_male",
-    "workclass",
-    "over_threshold",
-]
-_ENCODING_DICS = {
-    "is_male": {
-            "Male": True,
-            "Female": False
-        }
-}
-_RACE_ENCODING = {
-    "White": 0,
-    "Black": 1,
-    "Asian-Pac-Islander": 2,
-    "Amer-Indian-Eskimo": 3,
-    "Other": 4,
-}
-_EDUCATION_ENCODING = {
-	"Preschool": 0,
-	"1st-4th": 1,
-	"5th-6th": 2,
-	"7th-8th": 3,
-	"9th": 4,
-	"10th": 5,
-	"11th": 6,
-	"12th": 7,
-	"HS-grad": 8,
-	"Assoc-acdm": 9,
-	"Assoc-voc": 10,
-	"Some-college": 11,
-    "Bachelors": 12,
-	"Masters": 13,
-	"Doctorate": 14,
-	"Prof-school": 15
-}
-
-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/main/adult_tr.csv", 
-    "test": "https://huggingface.co/datasets/mstz/adult/raw/main/adult_ts.csv"
-}
-features_types_per_config = {
-    "encoding": {
-        "feature": datasets.Value("string"),
-        "original_value": datasets.Value("string"),
-        "encoded_value": datasets.Value("int64"),
-    },
-    "income": {
-        "age": datasets.Value("int64"),
-        "capital_gain": datasets.Value("float64"),
-        "capital_loss": datasets.Value("float64"),
-        "education": datasets.Value("int8"),
-        "final_weight": datasets.Value("int64"),
-        "hours_worked_per_week": datasets.Value("int64"),
-        "marital_status": datasets.Value("string"),
-        "native_country": datasets.Value("string"),
-        "occupation": datasets.Value("string"),
-        "race": datasets.Value("string"),
-        "relationship": datasets.Value("string"),
-        "is_male": datasets.Value("bool"),
-        "workclass": datasets.Value("string"),
-        "over_threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-    },
-    "income-no race": {
-        "age": datasets.Value("int64"),
-        "capital_gain": datasets.Value("float64"),
-        "capital_loss": datasets.Value("float64"),
-        "education": datasets.Value("int64"),
-        "final_weight": datasets.Value("int64"),
-        "hours_worked_per_week": datasets.Value("int64"),
-        "marital_status": datasets.Value("string"),
-        "native_country": datasets.Value("string"),
-        "occupation": datasets.Value("string"),
-        "relationship": datasets.Value("string"),
-        "is_male": datasets.Value("bool"),
-        "workclass": datasets.Value("string"),
-        "over_threshold": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-    },
-    "race": {
-        "age": datasets.Value("int64"),
-        "capital_gain": datasets.Value("float64"),
-        "capital_loss": datasets.Value("float64"),
-        "education": datasets.Value("int64"),
-        "final_weight": datasets.Value("int64"),
-        "hours_worked_per_week": datasets.Value("int64"),
-        "marital_status": datasets.Value("string"),
-        "native_country": datasets.Value("string"),
-        "occupation": datasets.Value("string"),
-        "relationship": datasets.Value("string"),
-        "is_male": datasets.Value("bool"),
-        "workclass": datasets.Value("string"),
-        "over_threshold": datasets.Value("int8"),
-        "race": datasets.ClassLabel(num_classes=5, names=["White", "Black", "Asian-Pac-Islander", "Amer-Indian-Eskimo", "Other"])
-    }
-}
-features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
-
-
-class AdultConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(AdultConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Adult(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "income"
-    BUILDER_CONFIGS = [
-        AdultConfig(name="income",
-                    description="Adult for income threshold binary classification."),
-        AdultConfig(name="income-no race",
-                    description="Adult for income threshold binary classification, race excluded from features."),
-        AdultConfig(name="race", 
-                    description="Adult for race (multiclass) classification."),
-        AdultConfig(name="encoding",
-                    description="Encoding dictionaries for discrete features.")
-    ]
-
-
-    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"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"]}),
-        ]
-    
-    def _generate_examples(self, filepath: str):
-        if self.config.name == "encoding":
-            data = self.encodings()
-
-            for row_id, row in data.iterrows():
-                data_row = dict(row)
-
-                yield row_id, data_row
-
-        else:
-            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 encodings(self):
-        data = pandas.DataFrame([("education", original_value, encoded_value)
-                                 for original_value, encoded_value in _EDUCATION_ENCODING.items()],
-                                columns=["feature", "original_value", "encoded_value"])
-
-        return data
-
-
-    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
-        data.drop("education", axis="columns", inplace=True)
-        data = data.rename(columns={"threshold": "over_threshold", "sex": "is_male"})
-
-        data = data[["age", "capital_gain", "capital_loss", "education-num", "final_weight",
-                     "hours_per_week", "marital_status", "native_country", "occupation",
-                     "race", "relationship", "is_male", "workclass", "over_threshold"]]
-        data.columns = _BASE_FEATURE_NAMES
-
-        for feature in _ENCODING_DICS:
-            encoding_function = partial(self.encode, feature)
-            data.loc[:, feature] = data[feature].apply(encoding_function)
-        
-
-        if config == "income":
-            return data[list(features_types_per_config["income"].keys())]
-        elif config == "income-no race":
-            return data[list(features_types_per_config["income-no race"].keys())]
-        elif config =="race":
-            data.loc[:, "race"] = data.race.apply(self.encode_race)
-            data = data[list(features_types_per_config["race"].keys())]
-
-            return data
-    
-    def encode(self, feature, value):
-        if feature in _ENCODING_DICS:
-            return _ENCODING_DICS[feature][value]
-        raise ValueError(f"Unknown feature: {feature}")
-    
-    def encode_race(self, race):
-        return _RACE_ENCODING[race]