evaluation_code/README.md

# How to run the evaluation codes

### Data Preparation and Organization

The evaluation code assumes the following structure.
If you want to use a different structure you can consider changing the `X_DIR` variables in `src/utils`

```
root_folder
├──src
│   └──eval codes (the directory this README is in)
└──data (DATA_DIR)
    ├──results
    │   └──experiment_results.pkl
    ├──bird_data (SQLITE_DB_DIR)
    │   ├──db_name
    │   │   └──db_name.sqlite
    │   └──...
    ├──dp-bench (BENCH_DIR)
    │   ├──v7_dp_bench_with_topics.json
    │   └──v7_hard_dp_bench_with_topics.json
    └──bird_elt_dp
        └──bird_db_schema (SCHEMA_DIR)
            ├──bird_train_tables.json
            └──bird_dev_tables.json
```

The SQLite DBs should be downloaded from [https://bird-bench.github.io/](https://bird-bench.github.io/) and organized
into a single directory, such that each database's DB is under a directory like `bird_data/address/address.sqlite`

## Environment setup

Create a venv and install required packages found in `requirements.txt`

## Running evaluations

Basic evaluation code is found in `src/dp_bench_evaluations.py`.

`calc_results` will take a file with data product predictions and input and calculate various metrics against the 
ground-truth data product data.

`eval_text2sql` will evaluate the accuracy of text2sql experiments.

Example usage:
```python
with open((BENCH_DIR / 'v7_dp_bench_with_topics.json').resolve(), 'r') as f:
    dpr_data = json.load(f)

with open((SCHEMA_DIR / 'bird_train_tables.json').resolve(), 'r') as f:
    raw_schema_data = json.load(f)
with open((SCHEMA_DIR / 'bird_dev_tables.json').resolve(), 'r') as f:
    raw_schema_data |= json.load(f)

dp_result_file = 'dpbench_baseline_gptoss_120b.pkl'
with open((DATA_DIR / 'results' / dp_result_file).resolve(), 'rb') as f:
    experiment_res = pickle.load(f)
print(f"Results for {dp_result_file}")
calc_results(experiment_res, dpr_data, schema_data=raw_schema_data, evaluate_derived_columns_soft_match=True)

text2sql_result_file = 'llama4_gptoss_120b_text2sql.json'
with open((DATA_DIR / 'results' / text2sql_result_file).resolve(), 'r') as f:
    text2sql_experiment_res = json.load(f)
print(f"Results for {text2sql_result_file}")
eval_text2sql(text2sql_experiment_res)
```

Experiment results are expected to be formatted as follows:

For data product generation results, we currently expect a dictionary formatted as follows:
```json
{
  'address': [
    {
      'DPR': 'Collect data that will allow queries on geographic and demographic characteristics, including population, households, and economic information, as well as details about employment, congressional representatives and postal services, allowing for insights into various aspects of residential areas and their surroundings.',
      'PREDICTION': {
        'zip_data': ['zip_code', 'city', 'state', ...],
        'area_code': [...],
        ...,
        'DERIVED_COLUMNS': [
          {
            'name': 'population_density',
            'SQL': 'SELECT zip_code, (CAST(population_2020 AS REAL) / NULLIF(land_area,0)) AS population_density FROM zip_data;',
            'source_columns': {
              'zip_data': ['zip_code',
                'population_2020',
                'land_area']
            }
          },
          ...
        ]
      }
    },
    {
      'DPR': ...,
      ...
    }
  ],
  'db_name': [...],
  ...
}
```
Each database is a key in the dict, and the value is a list of predicted data products for each DPR in our benchmark.
Data product `PREDICTION` contents are dictionaries, where tables and columns to include in the data product are key/values. 
Any Derived Columns produced as part of the prediction are included under `DERIVED_COLUMNS`.

For Text2SQL results, we expect a list of dictionaries formatted like
```json
[
{'database': 'address',
 'question': 'Which residential area in Arecibo county has the highest average house value? Please give its zip_code.',
 'GT': "SELECT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' ORDER BY T1.avg_house_value DESC LIMIT 1",
 'pred': "SELECT T1.zip_code FROM zip_data AS T1 INNER JOIN country AS T2 ON T1.zip_code = T2.zip_code WHERE T2.county = 'ARECIBO' ORDER BY T1.avg_house_value DESC LIMIT 1;"}
...
]
```