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README.md

@@ -18,3 +18,21 @@ configs:
 ---
 # Student performance
 The [Student performance dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/student_performances) is cool.
+
+# Configurations and tasks
+- `encoding`, encoding dictionaries mapping binary and ordinal features to their value;
+- `math` binary classification: has the student passed the math exam?
+- `writing` binary classification: has the student passed the writing exam?
+- `reading` binary classification: has the student passed the reading exam?
+
+# Features
+|**Feature**                        |**Type**   |
+|-----------------------------------|-----------|
+|`sex`                              |`int8`     |
+|`ethnicity`                        |`string`   |
+|`parental_level_of_education`      |`int8`     |
+|`has_standard_lunch`               |`int8`     |
+|`has_completed_preparation_test`   |`string`   |
+|`reading_score`                    |`int64`    |
+|`writing_score`                    |`int64`    |
+|`math_score`                       |`int64`    |

student_performance.py

@@ -130,8 +130,13 @@ class StudentPerformance(datasets.GeneratorBasedBuilder):
         ]
     
     def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath)
-        data = self.preprocess(data, config=self.config.name)
+        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)
@@ -139,9 +144,6 @@ class StudentPerformance(datasets.GeneratorBasedBuilder):
             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 = _BASE_FEATURE_NAMES
         for feature in _ENCODING_DICS:
             encoding_function = partial(self.encode, feature)
@@ -162,22 +164,16 @@ class StudentPerformance(datasets.GeneratorBasedBuilder):
             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())]
-        else:
-            raise ValueError(f"Unknown config: {config}")              
+
 
     def encode(self, feature, value):
         return _ENCODING_DICS[feature][value]
     
     def encoding_dics(self):
-        print("encoding...\n\n\n")
         data = [pandas.DataFrame([(feature, original, encoded) for original, encoded in d.items()])
                 for feature, d in _ENCODING_DICS.items()]
-        print("done...\n\n\n")
         data = pandas.concat(data, axis="rows").reset_index()
         data.drop("index", axis="columns", inplace=True)
-        print(data)
-        print(data.dtypes)
         data.columns = ["feature", "original_value", "encoded_value"]
-        print("done...\n\n\n")
 
         return data