"""Spect"""

from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

DESCRIPTION = "Spect dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Spect"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Spect")
_CITATION = """
@misc{misc_spect_heart_95,
  author       = {Cios,Krzysztof, Kurgan,Lukasz & Goodenday,Lucy},
  title        = {{SPECT Heart}},
  year         = {2001},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5P304}}
}"""

# Dataset info
urls_per_split = {
    "spect": {
		"train": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECT.train",
    	"test": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECT.test"
	},
	"spectf": {
		"train": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECTF.train",
    	"test": "https://huggingface.co/datasets/mstz/spect/raw/main/SPECTF.test"
	}
}
features_types_per_config = {
    "spect": {
		"feature_0": datasets.Value("bool"),
		"feature_1": datasets.Value("bool"),
		"feature_2": datasets.Value("bool"),
		"feature_3": datasets.Value("bool"),
		"feature_4": datasets.Value("bool"),
		"feature_5": datasets.Value("bool"),
		"feature_6": datasets.Value("bool"),
		"feature_7": datasets.Value("bool"),
		"feature_8": datasets.Value("bool"),
		"feature_9": datasets.Value("bool"),
		"feature_10": datasets.Value("bool"),
		"feature_11": datasets.Value("bool"),
		"feature_12": datasets.Value("bool"),
		"feature_13": datasets.Value("bool"),
		"feature_14": datasets.Value("bool"),
		"feature_15": datasets.Value("bool"),
		"feature_16": datasets.Value("bool"),
		"feature_17": datasets.Value("bool"),
		"feature_18": datasets.Value("bool"),
		"feature_19": datasets.Value("bool"),
		"feature_20": datasets.Value("bool"),
		"feature_21": datasets.Value("bool"),
		"is_emitted": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
    },
    "spectf": {
		"F1R": datasets.Value("int8"),
		"F1S": datasets.Value("int8"),
		"F2R": datasets.Value("int8"),
		"F2S": datasets.Value("int8"),
		"F3R": datasets.Value("int8"),
		"F3S": datasets.Value("int8"),
		"F4R": datasets.Value("int8"),
		"F4S": datasets.Value("int8"),
		"F5R": datasets.Value("int8"),
		"F5S": datasets.Value("int8"),
		"F6R": datasets.Value("int8"),
		"F6S": datasets.Value("int8"),
		"F7R": datasets.Value("int8"),
		"F7S": datasets.Value("int8"),
		"F8R": datasets.Value("int8"),
		"F8S": datasets.Value("int8"),
		"F9R": datasets.Value("int8"),
		"F9S": datasets.Value("int8"),
		"F10R": datasets.Value("int8"),
		"F10S": datasets.Value("int8"),
		"F11R": datasets.Value("int8"),
		"F11S": datasets.Value("int8"),
		"F12R": datasets.Value("int8"),
		"F12S": datasets.Value("int8"),
		"F13R": datasets.Value("int8"),
		"F13S": datasets.Value("int8"),
		"F14R": datasets.Value("int8"),
		"F14S": datasets.Value("int8"),
		"F15R": datasets.Value("int8"),
		"F15S": datasets.Value("int8"),
		"F16R": datasets.Value("int8"),
		"F16S": datasets.Value("int8"),
		"F17R": datasets.Value("int8"),
		"F17S": datasets.Value("int8"),
		"F18R": datasets.Value("int8"),
		"F18S": datasets.Value("int8"),
		"F19R": datasets.Value("int8"),
		"F19S": datasets.Value("int8"),
		"F20R": datasets.Value("int8"),
		"F20S": datasets.Value("int8"),
		"F21R": datasets.Value("int8"),
		"F21S": datasets.Value("int8"),
		"F22R": datasets.Value("int8"),
		"F22S": datasets.Value("int8"),
	    "is_emitted": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
    },
    
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class SpectConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(SpectConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Spect(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "spect"
    BUILDER_CONFIGS = [
        SpectConfig(name="spect",
                    description="Spect for binary classification."),
        SpectConfig(name="spectf",
                    description="Spectf for binary classification.")
        ]


    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[self.config.name]["train"]})
        ]
    
    def _generate_examples(self, filepath: str):
        data = pandas.read_csv(filepath, header=None)
        features = list(features_types_per_config[self.config.name])
        base_features = [features[-1]] + features[:-1]
        data.columns = base_features
        data = data[features]

        for row_id, row in data.iterrows():
            data_row = dict(row)

            yield row_id, data_row