Datasets:

mstz
/

student_performance

	"""StudentPerformance Dataset"""

	from typing import List
	from functools import partial

	import datasets

	import pandas


	VERSION = datasets.Version("1.0.0")
	_BASE_FEATURE_NAMES = [
	"sex",
	"ethnicity",
	"parental_level_of_education",
	"has_standard_lunch",
	"has_completed_preparation_test",
	"math_score",
	"reading_score",
	"writing_score"
	]

	_ENCODING_DICS = {
	"sex": {
	"female": 0,
	"male": 1
	},
	"parental_level_of_education": {
	"some high school": 0,
	"high school": 1,
	"some college": 2,
	"bachelor's degree": 3,
	"master's degree": 4,
	"associate's degree": 5,
	},
	"has_standard_lunch" : {
	"free/reduced": 0,
	"standard": 1
	},
	"has_completed_preparation_test": {
	"none": 0,
	"completed": 1
	}
	}

	DESCRIPTION = "StudentPerformance dataset."
	_HOMEPAGE = "https://www.kaggle.com/datasets/ulrikthygepedersen/student_performances"
	_URLS = ("https://www.kaggle.com/datasets/ulrikthygepedersen/student_performances")
	_CITATION = """"""

	# Dataset info
	urls_per_split = {
	"train": "https://huggingface.co/datasets/mstz/student_performance/raw/main/student_performance.csv",
	}
	features_types_per_config = {
	"encoding": {
	"feature": datasets.Value("string"),
	"original_value": datasets.Value("string"),
	"encoded_value": datasets.Value("int64")
	},
	"math": {
	"is_male": datasets.Value("bool"),
	"ethnicity": datasets.Value("string"),
	"parental_level_of_education": datasets.Value("int8"),
	"has_standard_lunch": datasets.Value("bool"),
	"has_completed_preparation_test": datasets.Value("bool"),
	"reading_score": datasets.Value("int64"),
	"writing_score": datasets.Value("int64"),
	"has_passed_math_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
	},
	"writing": {
	"is_male": datasets.Value("bool"),
	"ethnicity": datasets.Value("string"),
	"parental_level_of_education": datasets.Value("int8"),
	"has_standard_lunch": datasets.Value("bool"),
	"has_completed_preparation_test": datasets.Value("bool"),
	"reading_score": datasets.Value("int64"),
	"math_score": datasets.Value("int64"),
	"has_passed_writing_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
	},
	"reading": {
	"is_male": datasets.Value("bool"),
	"ethnicity": datasets.Value("string"),
	"parental_level_of_education": datasets.Value("int8"),
	"has_standard_lunch": datasets.Value("bool"),
	"has_completed_preparation_test": datasets.Value("bool"),
	"writing_score": datasets.Value("int64"),
	"math_score": datasets.Value("int64"),
	"has_passed_reading_exam": 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 StudentPerformanceConfig(datasets.BuilderConfig):
	def __init__(self, **kwargs):
	super(StudentPerformanceConfig, self).__init__(version=VERSION, **kwargs)
	self.features = features_per_config[kwargs["name"]]


	class StudentPerformance(datasets.GeneratorBasedBuilder):
	# dataset versions
	DEFAULT_CONFIG = "math"
	BUILDER_CONFIGS = [
	StudentPerformanceConfig(name="encoding",
	description="Encoding dictionaries."),
	StudentPerformanceConfig(name="math",
	description="Binary classification, predict if the student has passed the math exam."),
	StudentPerformanceConfig(name="reading",
	description="Binary classification, predict if the student has passed the reading exam."),
	StudentPerformanceConfig(name="writing",
	description="Binary classification, predict if the student has passed the writing exam."),
	]


	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"]}),
	]

	def _generate_examples(self, filepath: str):
	if self.config.name not in features_types_per_config:
	raise ValueError(f"Unknown config: {self.config.name}")
	elif self.config.name == "encoding":
	data = self.encoding_dics()
	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 preprocess(self, data: pandas.DataFrame, config: str = "math") -> pandas.DataFrame:
	data.columns = _BASE_FEATURE_NAMES
	for feature in _ENCODING_DICS:
	encoding_function = partial(self.encode, feature)
	data.loc[:, feature] = data[feature].apply(encoding_function)
	data = data.rename(columns={"sex": "is_male"})
	data = data.astype({"is_male": "bool", "has_standard_lunch": "bool", "has_completed_preparation_test": "bool"})

	if config == "math":
	data = data.rename(columns={"math_score": "has_passed_math_exam"})
	data.loc[:, "has_passed_math_exam"] = data.has_passed_math_exam.apply(lambda x: int(x > 60))

	return data[list(features_types_per_config["math"].keys())]
	elif config == "reading":
	data = data.rename(columns={"reading_score": "has_passed_reading_exam"})
	data.loc[:, "has_passed_reading_exam"] = data.has_passed_reading_exam.apply(lambda x: int(x > 60))

	return data[list(features_types_per_config["reading"].keys())]
	elif config == "writing":
	data = data.rename(columns={"writing_score": "has_passed_writing_exam"})
	data.loc[:, "has_passed_writing_exam"] = data.has_passed_writing_exam.apply(lambda x: int(x > 60))

	return data[list(features_types_per_config["writing"].keys())]


	def encode(self, feature, value):
	return _ENCODING_DICS[feature][value]

	def encoding_dics(self):
	data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()])
	for feature, d in _ENCODING_DICS.items()]
	data = pandas.concat(data, axis="rows").reset_index()
	data.drop("index", axis="columns", inplace=True)
	data.columns = ["feature", "original_value", "encoded_value"]

	return data