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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- tabular-classification
+- regression
+tags:
+- operations-research
+- 2d-nesting
+- irregular-strip-packing
+- geometry
+- graph-neural-networks
+- surrogate-modeling
+pretty_name: Nesting Tasks Dataset for 2D Nesting Efficiency Estimation
+dataset_info:
+  splits:
+  - name: train
+    num_examples: 100000
+---
+
+# Nesting Tasks Dataset for 2D Nesting Efficiency Estimation
+
+This is the official Hugging Face Hub version of the **2D Nesting Tasks Dataset** originally published on Zenodo ([DOI: 10.5281/zenodo.7030786](https://doi.org/10.5281/zenodo.7030786)), in 2022, during the my PhD research.
+
+## 👥 Authors & Affiliations
+
+*   **Corentin Lallier** (University of Bordeaux / @ Lectra) — [🎓 Google Scholar](https://scholar.google.com/citations?user=73Tg-wkAAAAJ) | [💼 LinkedIn](https://www.linkedin.com/in/corentin-lallier/) | [💻 GitHub](https://github.com/clallier) | [🆔 ORCID](https://orcid.org/0000-0003-0518-8308)
+*   **Laurent Vézard** (Data Science Manager @ Lectra) — [🎓 Google Scholar](https://scholar.google.com/citations?hl=fr&user=gQv0TgMAAAAJ) | [💼 LinkedIn](https://www.linkedin.com/in/laurent-v%C3%A9zard-36822280/)
+*   **Bruno Pinaud** (Associate Professor @ University of Bordeaux / LaBRI) — [🏫 LaBRI Page](http://www.labri.fr/~bpinaud/) | [🆔 ORCID](https://orcid.org/0000-0003-4814-3273)
+*   **Guillaume Blin** (Professor @ University of Bordeaux / LaBRI) — [🏫 LaBRI Page](http://www.labri.fr/~gblin/) | [🆔 ORCID](https://orcid.org/0000-0002-0708-0838)
+
+---
+
+This dataset is designed for training **machine learning surrogate models** (such as Graph Neural Networks) to estimate 2D irregular nesting efficiency (material utilization) without running computationally expensive operations research packing heuristics.
+
+---
+
+## 📂 Dataset Summary
+
+2D irregular cutting and packing (nesting) is a critical optimization problem in industries like textiles, apparel, and sheet-metal manufacturing. Traditionally, calculating the layout and material utilization of a set of irregular polygons requires running complex nesting heuristics, which can take seconds to minutes per run.
+
+This dataset provides **100,000 unique nesting tasks** containing high-level task descriptions, irregular polygon shapes, constraints, and target nesting efficiencies. This enables the training of neural networks to predict material utilization instantly, facilitating rapid layout evaluations.
+
+---
+
+## 🧬 Data Schema & Description
+
+The dataset is divided into four modern, secure, and highly optimized **Apache Parquet** files:
+
+### 1. `tasks.parquet` (Nesting high-level descriptors)
+Contains global metrics and metadata for each nesting task.
+
+| Column | Type | Description |
+| :--- | :--- | :--- |
+| `efficiency` | `float` | **The target label to predict.** Given in percentage (`%`). |
+| `duration` | `integer` | Nesting algorithm convergence time in seconds (`s`). |
+| `sheet_width` | `integer` | Width of the nesting area (unit: $m^{-4}$). |
+| `sheet_length`| `integer` | Length of the nesting area (unit: $m^{-4}$). |
+| `sheet_type`  | `integer` | Specific nesting classification category. |
+| `tasks_index` | `integer` | **Join key** connecting tables across files. |
+| `is_train` / `is_val` / `is_test` | `boolean` | Masks indicating standard partitions for training, validation, and testing. |
+
+---
+
+### 2. `parts.parquet` (Description of parts to be nested)
+Describes the specific instances of parts allocated to each nesting task.
+
+| Column | Type | Description |
+| :--- | :--- | :--- |
+| `tasks_index` | `integer` | Reference to `tasks_index` in the `tasks` file. |
+| `parts_id` | `integer` | Unique identifier for the part within its specific nesting task. |
+| `shape_hash` | `integer` | Hash of the part's shape, serving as the **join key** to the `shapes` file. |
+
+---
+
+### 3. `shapes.parquet` (Coordinate shapes of irregular polygons)
+Detailed geometry coordinate boundaries for all irregular parts.
+
+| Column | Type | Description |
+| :--- | :--- | :--- |
+| `shape_hash` | `integer` | Unique hash identifier of the shape geometry. |
+| `raw` | `list of integers` | Alternating sequence of $(x, y)$ coordinates defining the shape outline. |
+| `sizes` | `list of integers` | Vertex sizes of any sub-shapes or inner boundaries. |
+
+---
+
+### 4. `constraints.parquet` (Spacing, rotation, and alignment parameters)
+Geometric spacing boundaries and physical placement constraints.
+
+| Column | Type | Description |
+| :--- | :--- | :--- |
+| `type` | `string` | Specific constraint category type identifier. |
+| `tasks_index` | `integer` | Reference to the nesting task `tasks_index`. |
+| `parts_1`, `parts_2` | `list of integers` | Part IDs involved in the constraint. |
+| `p1_x, p1_y / p2_x, p2_y` | `list of floats` | Spacing anchors and offset coordinates on each part. |
+| `r1_start, r1_end, r1_flip_x`| `list of floats` | Allowed rotation range and flip settings. |
+| `y_min, y_max` | `list of floats` | Allowed positioning range on the $y$-axis. |
+
+---
+
+## 🚀 How to Load and Explore the Dataset
+
+Since the dataset is stored in standard Apache Parquet format, loading takes a single line of python code:
+
+```python
+import pandas as pd
+
+# Load the high-level nesting tasks and labels
+tasks_df = pd.read_parquet('tasks.parquet')
+
+# Load corresponding geometric parts
+parts_df = pd.read_parquet('parts.parquet')
+
+# Load irregular polygon shape definitions
+shapes_df = pd.read_parquet('shapes.parquet')
+
+# Load spacing and alignment constraints
+constraints_df = pd.read_parquet('constraints.parquet')
+
+# Filter splits using the pre-defined boolean masks in tasks.parquet
+train_df = tasks_df[tasks_df['is_train']]
+val_df = tasks_df[tasks_df['is_val']]
+test_df = tasks_df[tasks_df['is_test']]
+
+print(f"Loaded {len(tasks_df)} irregular nesting tasks:")
+print(f"   🟢 Train instances      : {len(train_df)}")
+print(f"   🟡 Validation instances : {len(val_df)}")
+print(f"   🔴 Test instances       : {len(test_df)}")
+```
+
+---
+
+## 🛠️ Handling Missing Values (Nulls)
+
+In the binary Apache Parquet format, missing optional parameters (such as unassigned `x_offset` or `y_min` values in `constraints.parquet`) are stored natively as **`NULL`** slots using Parquet's definition levels. 
+
+Different programming languages and frameworks load these binary `NULL`s into their own native type-safe representations:
+
+*   **Python (Pandas):** 
+    *   Native numerical columns (like `x_offset` of type `float64`) represent missing values as **`NaN`** (float representation).
+    *   Object or list columns (like `y_min` of type `object`) represent missing values as **`None`**.
+    *   *Tip:* You can check for both formats simultaneously using a single call to `df.isna()` or `df.isnull()`.
+*   **Rust (Polars / Arrow):** 
+    *   Parsed directly into native, type-safe optional wrappers: **`Option<f64>`** for float parameters and **`Option<Vec<f64>>`** for geometric coordinate lists.
+*   **C++ (Arrow):** 
+    *   Represented as null slots in the Arrow array validity bitmap (`IsValid(index) == false`).
+
+
+---
+
+## 📜 Citation & Credits
+
+If you use this dataset in your research or industrial applications, please cite the original Zenodo record:
+
+```bibtex
+@dataset{lallier2022nesting,
+  author       = {Lallier, Corentin and V{\'e}zard, Laurent and Pinaud, Bruno and Blin, Guillaume},
+  title        = {Nesting tasks dataset for 2d-nesting efficiency estimation},
+  month        = aug,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {1.1.0},
+  doi          = {10.5281/zenodo.7030786},
+  url          = {https://doi.org/10.5281/zenodo.7030786}
+}
+```

constraints.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35147f906ebbcf613f6b3f96ccf688659f68ea2422236768199f2100a8bb1875
+size 7580727

download_dataset.py

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+# /// 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()

parts.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:131ec25d1f02100c05ad92282f1c76b058bc4d00ad05686e6d5510ce06b22017
+size 16956526

shapes.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cf09a0d2c815ff2fba814e309c33649bc909210c260dcf8c51650cf2afb0bf74
+size 141312398

tasks.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:610d13b456f6353113ac39c28499ea2000b84c03995f8ae126256e96c8aca718
+size 1891739