Refactor conversion script

README.md

@@ -15,12 +15,11 @@ python -m pip install -r requirements.txt
 You can then run the script as follows:
 
 ```python
-python convert_datasets_to_json.py
+python generate_evaluation_datasets.py
 ```
 
 This script will:
 
 * Download and convert the datasets under the GEM organisation to JSON format
-* Download the corresponding reference datasets from the GitHub release to the `github_references/` folder
-* Perform a diff validation between the converted and original reference datasets via the `jq` library
+* Validate that the each dataset has the expected columns of `gem_id`, `target`, and `references`
 

generate_evaluation_datasets.py

@@ -0,0 +1,97 @@
+import typer
+from datasets import (Dataset, DatasetDict, get_dataset_config_names,
+                      load_dataset)
+from huggingface_hub import list_datasets
+import pandas as pd
+
+app = typer.Typer()
+
+
+def convert(dataset_id: str):
+    errors = []
+    dataset_name = dataset_id.split("/")[-1]
+    try:
+        configs = get_dataset_config_names(dataset_id)
+    except:
+        typer.echo(f"❌ Failed to get configs for {dataset_id}")
+        errors.append({"dataset_name": dataset_id, "error_type": "config"})
+        return errors
+
+    for config in configs:
+        typer.echo(f"🛠️🛠️🛠️ Converting {dataset_id} with config {config} 🛠️🛠️🛠️")
+        try:
+            raw_datasets = load_dataset(dataset_id, name=config)
+        except:
+            typer.echo(f"❌ Failed to load {dataset_id} with config {config}")
+            errors.append({"dataset_name": dataset_id, "config": config, "error_type": "load"})
+            continue
+        datasets_to_convert = DatasetDict()
+
+        for split, dataset in raw_datasets.items():
+            if split not in ["train", "validation"]:
+                datasets_to_convert[split] = dataset
+
+        for split, dataset in datasets_to_convert.items():
+            columns_to_keep = ["gem_id", "target", "references"]
+            remainder_cols = validate_columns(dataset)
+            if len(remainder_cols) > 0:
+                typer.echo(
+                    f"❌ Skipping {dataset_name}/{config}/{split} due to missing columns: {', '.join(remainder_cols)}"
+                )
+                errors.append({"dataset_name": dataset_id, "config": config, "split": split, "error_type": "missing_columns", "missing_columns": remainder_cols})
+            else:
+                # Add `input` column if it exists
+                if "input" in dataset.column_names:
+                    columns_to_keep.append("input")
+                # The test split doesn't have a parent ID
+                # TODO(lewtun): check this logic!
+                if split != "test" and "gem_parent_id" in dataset.column_names:
+                    columns_to_keep.append("gem_parent_id")
+                # The `datasets` JSON serializer is buggy - use `pandas` for now
+                df = dataset.to_pandas()
+                # Exclude dummy config names for comparison with GitHub source dataset
+                if config in ["default", "xsum", "totto"]:
+                    reference_name = f"{dataset_name}_{split}"
+                else:
+                    reference_name = f"{dataset_name}_{config}_{split}"
+                df[columns_to_keep].to_json(f"{reference_name}.json", orient="records")
+
+        typer.echo(f"✅ Successfully converted {dataset_id} with config {config}")
+
+    return errors
+
+
+def validate_columns(dataset: Dataset):
+    ref_columns = ["gem_id", "target", "references"]
+    columns = dataset.column_names
+    return set(ref_columns) - set(columns)
+
+
+@app.command()
+def extract_evaluation_datasets():
+    errors = []
+    all_datasets = list_datasets()
+    # Filter for GEM datasets
+    gem_datasets = [dataset for dataset in all_datasets if dataset.id.startswith("GEM/")]
+    # Filter for blocklist
+    blocklist = [
+        "indonlg", # Can't load
+        "RiSAWOZ", # Can't load
+        "CrossWOZ", # Can't load
+        "references",  # This repo, so exclude!
+    ]
+    blocklist = ["GEM/" + dataset for dataset in blocklist]
+    gem_datasets = [dataset for dataset in gem_datasets if dataset.id not in blocklist]
+    for dataset in gem_datasets:
+        errors.extend(convert(dataset.id))
+
+    if len(errors):
+        typer.echo("🙈 Found conversion errors!")
+        errors_df = pd.DataFrame(errors)
+        errors_df.to_csv("conversion_errors.csv", index=False)
+
+    typer.echo(f"🥳 All datasets converted!")
+
+
+if __name__ == "__main__":
+    app()