Update README.md

README.md

@@ -210,3 +210,62 @@ If you use this dataset, please consider cite the following works (these might b
   year={2007},
   institution={Duke University}
 }
+```
+
+## The source
+Unfortunately, the source of this data is still a myth. This could by no means be the original data by [Wightman](https://files.eric.ed.gov/fulltext/ED469370.pdf) or [Richard Sander](https://www.brown.edu/Departments/Economics/Faculty/Glenn_Loury/louryhomepage/teaching/Ec%20137/Richard%20Sander%20on%20Affirmative%20Action%20in%20Law%20Schools.pdf), as these two papers were published before 2004.
+
+We roughly remember we came across a web page with the two papers in bibtex above, which could be the right page. Sadly, we cannot remember where it is, nor can we find it again. We really hope you can let us know the real source if you happen to know it.
+
+We used the following code to produce this Hugging Face dataset.
+
+```
+wget https://web.archive.org/web/20210427055113/http://www.seaphe.org/databases/FOIA/lawschs1_0.dta
+wget https://web.archive.org/web/20210427055113/http://www.seaphe.org/databases/FOIA/lawschs1_1.dta
+```
+
+(Note that we need to use this link. The original link is gone. Also, ```lawschs1_0.dta``` and ```lawschs1_1.dta``` only have < 5000 different rows out of > 120 k rows (if you happen to see number 1507 or 4556, then you are even closer). So we would just use ```lawschs1_1.dta```.)
+
+```python
+from datasets import Dataset, DatasetDict, Features, Value, ClassLabel
+
+# Define categorical and continuous columns
+continuous_columns = ["lsat", "gpa"]
+categorical_columns = ["race", "resident", "college", "year", "gender", "admit", "black",
+                       "hispanic", "asian", "white", "missingrace", "urm", "enroll"]
+
+# Convert categorical columns to category type and get mappings
+category_mappings = {}
+for col in categorical_columns:
+    # df_law[col] = df_law[col].astype("category")
+    category_mappings[col] = df_law[col].astype("category").cat.categories.to_list()
+    # df_law[col] = df_law[col].cat.codes  # Convert to integer codes
+
+# Define Hugging Face Dataset Features
+hf_features = Features({
+    "lsat": Value("int64"),
+    "gpa": Value("float32"),
+    "race": ClassLabel(names=category_mappings["race"]),
+    "resident": ClassLabel(names=['out-of-state', 'in-state']),
+    "college": ClassLabel(names=category_mappings["college"]),
+    "year": ClassLabel(names=[str(y) for y in category_mappings["year"]]),
+    "gender": ClassLabel(names=["female", "male"]),
+    "admit": ClassLabel(names=["denied", "admitted"]),
+    "black": ClassLabel(names=category_mappings["black"]),
+    "hispanic": ClassLabel(names=category_mappings["hispanic"]),
+    "asian": ClassLabel(names=category_mappings["asian"]),
+    "white": ClassLabel(names=category_mappings["white"]),
+    "missingrace": ClassLabel(names=category_mappings["missingrace"]),
+    "urm": ClassLabel(names=[0, 'Black OR Hispanic']),
+    "enroll": ClassLabel(names=["not enrolled", "enrolled"]),
+})
+
+# Convert to Hugging Face dataset
+hf_dataset = Dataset.from_pandas(df_law, features=hf_features)
+
+# Create DatasetDict for organization
+hf_dataset_dict = DatasetDict({"train": hf_dataset})
+
+# Display dataset structure
+hf_dataset_dict
+```