Delete student performance.py

student performance.py

@@ -1,175 +0,0 @@
-"""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 = {
-    "gender": {
-        "\"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_performances/raw/main/student_performances.csv",
-}
-features_types_per_config = {
-    "encoding": {
-        "feature": datasets.Value("string"),
-        "original_value": datasets.Value("string"),
-        "encoded_value": datasets.Value("int64")
-    },
-    "math": {
-        "sex": datasets.Value("int8"),
-        "ethnicity": datasets.Value("string"),
-        "parental_level_of_education": datasets.Value("int8"),
-        "has_standard_lunch": datasets.Value("int8"),
-        "test_preparation_course": datasets.Value("string"),
-        "reading_score": datasets.Value("int64"),
-        "writing_score": datasets.Value("int64"),
-        "has_passed_math_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-    },
-    "writing": {
-        "sex": datasets.Value("int8"),
-        "ethnicity": datasets.Value("string"),
-        "parental_level_of_education": datasets.Value("int8"),
-        "has_standard_lunch": datasets.Value("int8"),
-        "test_preparation_course": datasets.Value("string"),
-        "reading_score": datasets.Value("int64"),
-        "math_score": datasets.Value("int64"),
-        "has_passed_writing_exam": datasets.ClassLabel(num_classes=2, names=("no", "yes")),
-    },
-    "reading": {
-        "sex": datasets.Value("int8"),
-        "ethnicity": datasets.Value("string"),
-        "parental_level_of_education": datasets.Value("int8"),
-        "has_standard_lunch": datasets.Value("int8"),
-        "test_preparation_course": datasets.Value("string"),
-        "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):
-        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 = "cut") -> pandas.DataFrame:
-        if config == "encoding":
-            return self.encoding_dics()
-
-        data.columns = [c.replace("\"", "") for c in data.columns]
-
-        data.loc[:, "race/ethnicity"] = data["race/ethnicity"].apply(lambda x: x.replace("group ", ""))
-        
-        for feature in _ENCODING_DICS:
-            encoding_function = partial(self.encode, feature)
-            data.loc[:, feature] = data[feature].apply(encoding_function)
-         
-        data.columns = _BASE_FEATURE_NAMES
-        
-        if config == "math":
-            data = data.rename(colums={"math_score", "has_passed_math_exam"})
-            return data[list(features_types_per_config["math"].keys())]
-        elif config == "reading":
-            data = data.rename(colums={"reading_score", "has_passed_reading_exam"})
-            return data[list(features_types_per_config["reading"].keys())]
-        elif config == "writing":
-            data = data.rename(colums={"writing_score", "has_passed_writing_exam"})
-            return data[list(features_types_per_config["writing"].keys())]
-        else:
-            raise ValueError(f"Unknown config: {config}")              
-
-    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")
-
-        return data