| """Extract linguistic + math feature matrices for ASDiv (grade-labeled) and SVAMP.""" |
| import json |
| import pandas as pd |
| from datasets import load_dataset |
| from features import linguistic_features, math_features_from_chain, math_features_from_equation |
|
|
|
|
| def build_asdiv(): |
| ds = load_dataset("MU-NLPC/Calc-asdiv_a", split="test") |
| rows = [] |
| for ex in ds: |
| q = ex["question"] |
| if not q or not q.strip(): |
| continue |
| lf = linguistic_features(q) |
| mf = math_features_from_chain(ex["chain"] or "") |
| row = {"id": ex["id"], "grade": ex["grade"], "text": q} |
| row.update(lf) |
| row.update(mf) |
| rows.append(row) |
| df = pd.DataFrame(rows) |
| df.to_parquet("asdiv_features.parquet") |
| print("ASDiv:", df.shape, "grades:", sorted(df.grade.unique())) |
| print(df.grade.value_counts().sort_index().to_dict()) |
| return df |
|
|
|
|
| def build_svamp(): |
| ds = load_dataset("ChilleD/SVAMP", split="train") |
| rows = [] |
| for ex in ds: |
| text = (ex["Body"] + " " + ex["Question"]).strip() |
| lf = linguistic_features(text) |
| mf = math_features_from_equation(ex["Equation"] or "") |
| row = {"id": ex["ID"], "type": ex["Type"], "text": text} |
| row.update(lf) |
| row.update(mf) |
| rows.append(row) |
| df = pd.DataFrame(rows) |
| df.to_parquet("svamp_features.parquet") |
| print("SVAMP:", df.shape, "types:", df.type.nunique()) |
| return df |
|
|
|
|
| if __name__ == "__main__": |
| build_asdiv() |
| build_svamp() |
|
|
