"""Breast Cancer Wisconsin Dataset: African Physiognomy Adjusted"""

import csv
import datasets

_CITATION = """\
@misc{udodi2025breast,
  title={Addressing Representation Bias in Breast Cancer Datasets: A Physiognomy-Informed Approach for African Populations},
  author={Kossiso Udodi Royce},
  year={2025},
  publisher={Electric Sheep Africa},
  url={https://huggingface.co/datasets/ElectricSheepAfrica/breast-cancer-african-adjusted}
}
"""

_DESCRIPTION = """\
This dataset addresses representation bias in medical AI by providing an African physiognomy-adjusted 
version of the classic Wisconsin Breast Cancer Dataset. The adjustment methodology systematically 
modifies cellular morphology features to better reflect documented physiological differences in 
African populations.

Key adjustments include:
- Higher breast density (5-8% increase in size/texture features)
- Enhanced irregularity (12-19% increase in concavity/fractal features)  
- Reduced boundary smoothness (10-12% decrease in smoothness/symmetry)

The dataset contains 569 samples with 30 morphological features from Fine Needle Aspirate (FNA) 
samples, classified as Malignant (M) or Benign (B).
"""

_HOMEPAGE = "https://huggingface.co/datasets/ElectricSheepAfrica/breast-cancer-african-adjusted"

_LICENSE = "CC BY 4.0"

_URLS = {
    "african_adjusted": "breast_cancer_african_adjusted.csv",
    "wisconsin_breast_cancer_dataset": "breast_cancer_original.csv",
}

class BreastCancerAfricanAdjusted(datasets.GeneratorBasedBuilder):
    """Breast Cancer Wisconsin Dataset with African Physiognomy Adjustments"""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="african_adjusted",
            version=VERSION,
            description="African physiognomy-adjusted breast cancer dataset",
        ),
        datasets.BuilderConfig(
            name="wisconsin_breast_cancer_dataset",
            version=VERSION,
            description="Original Wisconsin breast cancer dataset",
        ),
    ]

    DEFAULT_CONFIG_NAME = "african_adjusted"

    def _info(self):
        features = datasets.Features({
            "id": datasets.Value("float64"),
            "diagnosis": datasets.Value("string"),
            "radius_mean": datasets.Value("float64"),
            "texture_mean": datasets.Value("float64"),
            "perimeter_mean": datasets.Value("float64"),
            "area_mean": datasets.Value("float64"),
            "smoothness_mean": datasets.Value("float64"),
            "compactness_mean": datasets.Value("float64"),
            "concavity_mean": datasets.Value("float64"),
            "concave points_mean": datasets.Value("float64"),
            "symmetry_mean": datasets.Value("float64"),
            "fractal_dimension_mean": datasets.Value("float64"),
            "radius_se": datasets.Value("float64"),
            "texture_se": datasets.Value("float64"),
            "perimeter_se": datasets.Value("float64"),
            "area_se": datasets.Value("float64"),
            "smoothness_se": datasets.Value("float64"),
            "compactness_se": datasets.Value("float64"),
            "concavity_se": datasets.Value("float64"),
            "concave points_se": datasets.Value("float64"),
            "symmetry_se": datasets.Value("float64"),
            "fractal_dimension_se": datasets.Value("float64"),
            "radius_worst": datasets.Value("float64"),
            "texture_worst": datasets.Value("float64"),
            "perimeter_worst": datasets.Value("float64"),
            "area_worst": datasets.Value("float64"),
            "smoothness_worst": datasets.Value("float64"),
            "compactness_worst": datasets.Value("float64"),
            "concavity_worst": datasets.Value("float64"),
            "concave points_worst": datasets.Value("float64"),
            "symmetry_worst": datasets.Value("float64"),
            "fractal_dimension_worst": datasets.Value("float64"),
        })

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS[self.config.name]
        data_file = dl_manager.download_and_extract(urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_file,
                    "split": "train",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f)
            for key, row in enumerate(reader):
                # Convert numeric fields
                for field in row:
                    if field != "diagnosis":
                        try:
                            row[field] = float(row[field])
                        except (ValueError, TypeError):
                            row[field] = None
                
                yield key, row