Delete download_dataset.py

download_dataset.py

@@ -1,158 +0,0 @@
-# /// script
-# dependencies = [
-#   "pandas",
-#   "pyarrow",
-#   "hf",
-# ]
-# ///
-
-"""Downloader script to fetch the Nesting Tasks Dataset from Zenodo."""
-
-import os
-import urllib.request
-import time
-
-
-def download_file(url: str, dest_path: str) -> None:
-    """Downloads a file with clean progress printouts.
-
-    Args:
-        url (str): The direct download URL.
-        dest_path (str): File destination path.
-    """
-    print(f"📥 Downloading {os.path.basename(dest_path)}...")
-    start_time = time.time()
-
-    def reporthook(count, block_size, total_size):
-        if total_size <= 0:
-            return
-        current_progress = count * block_size
-        percent = min(100, int(current_progress * 100 / total_size))
-        # Keep progress line on same terminal line
-        print(
-            f"\r   [{'=' * (percent // 5)}{' ' * (20 - percent // 5)}] {percent}% ({current_progress / (1024 * 1024):.1f}MB / {total_size / (1024 * 1024):.1f}MB)",
-            end="",
-            flush=True,
-        )
-
-    urllib.request.urlretrieve(url, dest_path, reporthook)  # nosec B310
-    duration = time.time() - start_time
-    print(f"\n   ✅ Completed in {duration:.1f}s!\n")
-
-
-def convert_to_parquet() -> None:
-    """Loads gzipped pickles with pandas and saves them as modern Parquet files.
-
-    Cleans up the raw .gz files afterwards to keep the repository secure and light.
-    """
-    import pandas as pd
-
-    files = ["tasks", "parts", "constraints", "shapes"]
-    print("============================================================")
-    print("🔄 Converting Pickle splits to Parquet format...")
-    print("============================================================")
-
-    for name in files:
-        pickle_file = f"{name}.gz"
-        parquet_file = f"{name}.parquet"
-
-        if not os.path.exists(pickle_file):
-            continue
-
-        print(f"⚡ Processing '{pickle_file}' -> '{parquet_file}'...")
-        try:
-            # 1. Read pickled dataframe
-            df = pd.read_pickle(pickle_file)
-
-            # 2. Write to Parquet (removing pandas index to keep schema clean)
-            df.to_parquet(parquet_file, index=False)
-            print(f"   ✅ Saved {parquet_file}")
-
-            # 3. Clean up the insecure raw pickle file
-            os.remove(pickle_file)
-            print(f"   🗑️ Removed raw {pickle_file}")
-        except Exception as err:
-            print(f"   ❌ Failed to convert {pickle_file}: {err}")
-            return
-    print()
-
-
-def main() -> None:
-    """Orchestrates the downloading of the Zenodo dataset files."""
-    print("============================================================")
-    print("📦 Zenodo Nesting Tasks Dataset Downloader")
-    print("============================================================")
-
-    # 1. Zenodo records API endpoints for version 1.1 of Lallier et al. (2022)
-    files_to_download = {
-        "tasks.gz": "https://zenodo.org/api/records/7030786/files/tasks.gz/content",
-        "parts.gz": "https://zenodo.org/api/records/7030786/files/parts.gz/content",
-        "constraints.gz": "https://zenodo.org/api/records/7030786/files/constraints.gz/content",
-        "shapes.gz": "https://zenodo.org/api/records/7030786/files/shapes.gz/content",
-    }
-
-    # 2. Iterate and download each file directly into workspace
-    for filename, url in files_to_download.items():
-        # Check if either the converted parquet or the raw .gz file already exists
-        parquet_name = filename.replace(".gz", ".parquet")
-        if os.path.exists(parquet_name):
-            print(
-                f"ℹ️ File '{parquet_name}' already exists locally (converted). Skipping download.\n"
-            )
-        elif os.path.exists(filename):
-            print(
-                f"ℹ️ File '{filename}' already exists locally (raw .gz). Skipping download.\n"
-            )
-        else:
-            try:
-                download_file(url, filename)
-            except Exception as err:
-                print(f"❌ Failed to download {filename}: {err}")
-                return
-
-    # 3. Perform automatic conversion and cleanup
-    convert_to_parquet()
-
-    # 4. Validate and pretty-print heads of all parquet files
-    print_dataset_head()
-
-    print("============================================================")
-    print("🎉 All dataset splits converted to Parquet successfully!")
-    print("============================================================")
-    print("💡 Next Step: To push this dataset to your Hugging Face profile, run:")
-    print("   $ uv run hf upload clallier/nesting-tasks-2d . --repo-type=dataset")
-    print("============================================================")
-
-
-def print_dataset_head() -> None:
-    """Loads and pretty-prints the first 10 rows of all Parquet files to verify conversion."""
-    import pandas as pd
-
-    # Configure pandas to show all columns without wrapping or ellipsis
-    pd.set_option("display.max_columns", None)
-    pd.set_option("display.width", 1000)
-
-    files = ["tasks", "parts", "constraints", "shapes"]
-    print("============================================================")
-    print("🔬 Verifying Parquet Schemas (First 10 rows of each split)")
-    print("============================================================")
-
-    for name in files:
-        parquet_file = f"{name}.parquet"
-        if not os.path.exists(parquet_file):
-            print(f"⚠️ Warning: '{parquet_file}' not found for validation.\n")
-            continue
-
-        print(f"\n📄 Split: {parquet_file}")
-        print("------------------------------------------------------------")
-        try:
-            df = pd.read_parquet(parquet_file)
-            print(df.head(10))
-        except Exception as err:
-            print(f"❌ Failed to read {parquet_file}: {err}")
-        print("------------------------------------------------------------")
-    print()
-
-
-if __name__ == "__main__":
-    main()