"""Planning: A Census Dataset"""

from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
    "V1",
	"V2",
	"V3",
	"V4",
	"V5",
	"V6",
	"V7",
	"V8",
	"V9",
	"V10",
	"V11",
	"V12",
	"class"
]

DESCRIPTION = "Planning dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Planning"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Planning")
_CITATION = """
@misc{misc_planning_relax_230,
  author       = {Bhatt,Rajen},
  title        = {{Planning Relax}},
  year         = {2012},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5T023}}
}"""

# Dataset info
urls_per_split = {
    "planning": {"train": "https://huggingface.co/datasets/mstz/planning/raw/main/plrx.csv"}
}
features_types_per_config = {
    "planning": {
		"V1": datasets.Value("float64"),
		"V2": datasets.Value("float64"),
		"V3": datasets.Value("float64"),
		"V4": datasets.Value("float64"),
		"V5": datasets.Value("float64"),
		"V6": datasets.Value("float64"),
		"V7": datasets.Value("float64"),
		"V8": datasets.Value("float64"),
		"V9": datasets.Value("float64"),
		"V10": datasets.Value("float64"),
		"V11": datasets.Value("float64"),
		"V12": datasets.Value("float64"),
		"class": 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 PlanningConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(PlanningConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Planning(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "planning"
    BUILDER_CONFIGS = [
       PlanningConfig(name="planning",
                    description="Planning 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)

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

            yield row_id, data_row