finish dataset parsing

aes_enem_dataset.py

@@ -47,7 +47,8 @@ _HOMEPAGE = ""
 _LICENSE = ""
 
 _URLS = {
-    "sourceAOnly": "https://huggingface.co/datasets/kamel-usp/aes_enem_dataset/resolve/main/sourceA.tar.gz?download=true",
+    "sourceAOnly": "sourceAWithGraders.tar.gz",
+    "sourceAWithGraders": "sourceAWithGraders.tar.gz",
     "sourceB": "https://huggingface.co/datasets/kamel-usp/aes_enem_dataset/resolve/main/sourceB.tar.gz?download=true",
 }
 
@@ -86,6 +87,9 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
     # You will be able to load one or the other configurations in the following list with
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name="sourceAOnly", version=VERSION, description="TODO"),
+        datasets.BuilderConfig(
+            name="sourceAWithGraders", version=VERSION, description="TODO"
+        ),
         datasets.BuilderConfig(
             name="sourceB",
             version=VERSION,
@@ -208,6 +212,24 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
         html_parser.parse(self.config.name)
         return html_parser
 
+    def _parse_graders_data(self, dirname):
+        map_grades = {"0": 0, "1": 40, "2": 80, "3": 120, "4": 160, "5": 200}
+
+        def map_list(grades_list):
+            result = [map_grades.get(item, None) for item in grades_list]
+            sum_grades = sum(result)
+            result.append(sum_grades)
+            return result
+
+        grader_a = pd.read_csv(f"{dirname}/GraderA.csv")
+        grader_b = pd.read_csv(f"{dirname}/GraderB.csv")
+
+        for grader in [grader_a, grader_b]:
+            grader.grades = grader.grades.apply(lambda x: x.strip("[]").split(", "))
+            grader.grades = grader.grades.apply(map_list)
+
+        return grader_a, grader_b
+
     def _generate_splits(self, filepath: str, train_size=0.7):
         df = pd.read_csv(filepath)
         buckets = df.groupby("mapped_year")["id_prompt"].unique().to_dict()
@@ -251,7 +273,53 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
         train_df = pd.concat(train_set)
         val_df = pd.concat(val_set)
         test_df = pd.concat(test_set)
-
+        dirname = os.path.dirname(filepath)
+        if self.config.name == "sourceAWithGraders":
+            grader_a, grader_b = self._parse_graders_data(dirname)
+            grader_a_data = pd.merge(
+                train_df[["id", "id_prompt"]],
+                grader_a,
+                on=["id", "id_prompt"],
+                how="inner",
+            )
+            grader_b_data = pd.merge(
+                train_df[["id", "id_prompt"]],
+                grader_b,
+                on=["id", "id_prompt"],
+                how="inner",
+            )
+            train_df = pd.concat([train_df, grader_a_data])
+            train_df = pd.concat([train_df, grader_b_data])
+
+            grader_a_data = pd.merge(
+                val_df[["id", "id_prompt"]],
+                grader_a,
+                on=["id", "id_prompt"],
+                how="inner",
+            )
+            grader_b_data = pd.merge(
+                val_df[["id", "id_prompt"]],
+                grader_b,
+                on=["id", "id_prompt"],
+                how="inner",
+            )
+            val_df = pd.concat([val_df, grader_a_data])
+            val_df = pd.concat([val_df, grader_b_data])
+
+            grader_a_data = pd.merge(
+                test_df[["id", "id_prompt"]],
+                grader_a,
+                on=["id", "id_prompt"],
+                how="inner",
+            )
+            grader_b_data = pd.merge(
+                test_df[["id", "id_prompt"]],
+                grader_b,
+                on=["id", "id_prompt"],
+                how="inner",
+            )
+            test_df = pd.concat([test_df, grader_a_data])
+            test_df = pd.concat([test_df, grader_b_data])
         # Data Validation Assertions
         assert (
             len(set(train_df["id_prompt"]).intersection(set(val_df["id_prompt"]))) == 0
@@ -262,8 +330,6 @@ class AesEnemDataset(datasets.GeneratorBasedBuilder):
         assert (
             len(set(val_df["id_prompt"]).intersection(set(test_df["id_prompt"]))) == 0
         ), "Overlap between val and test id_prompt"
-        # TODO if self.config.name == sourceAWithGrader
-        dirname = os.path.dirname(filepath)
         train_df.to_csv(f"{dirname}/train.csv", index=False)
         val_df.to_csv(f"{dirname}/validation.csv", index=False)
         test_df.to_csv(f"{dirname}/test.csv", index=False)
@@ -499,7 +565,7 @@ class HTMLParser:
             if key != config_name:
                 continue  # TODO improve later, we will only support a single config at a time
             if "sourceA" in config_name:
-                self.sourceA = f"{filepath}/sourceA/sourceA.csv"
+                self.sourceA = f"{filepath}/sourceAWithGraders/sourceA.csv"
             elif config_name == "sourceB":
                 self.sourceB = f"{filepath}/sourceB/sourceB.csv"
             file = self.sourceA if self.sourceA else self.sourceB