Upload student_performance.py

student_performance.py

@@ -0,0 +1,175 @@
+"""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