Skip to main content
zenodoopen

Nesting Tasks Dataset for 2D-Nesting Efficiency Estimation

<p>Nesting efficiency dataset</p> <p>This is the raw dataset associated with the paper &ldquo;Graph Neural Networks Comparison for 2D-Nesting Efficiency Estimation&rdquo;, by C.Lallier, L. V&eacute;zard, B. Pinaud and G. Blin,&nbsp;2022. Consisting of 100,000 nesting tasks.</p> <p><strong>Usage:</strong></p> <p>The files are: <em>tasks.gz,&nbsp;parts.gz,&nbsp;constraints.gz, </em>and<em>&nbsp;shapes.gz</em>.&nbsp;They&nbsp;are in&nbsp;PICKLE file format&nbsp;version 5 with a gzip compression. Example to load a file :</p> <pre><code class="language-python">import pandas as pd tasks = pd.read_pickle('tasks.gz')</code></pre> <p>&nbsp;</p> <p><strong>Description:</strong></p> <p><em>Tasks.gz</em> file contains nestings high-level descriptors. It is composed of the following&nbsp;columns:</p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <th scope="row">efficiency</th> <td>float</td> <td>The variable to predict (label). Given in %&nbsp;</td> </tr> <tr> <th scope="row">duration</th> <td>integer</td> <td>input data. The nesting algorithm convergence time. Given in s.</td> </tr> <tr> <th scope="row">sheet_width</th> <td>integer</td> <td>input data. The width of the nesting area. Given in m<sup>-4</sup></td> </tr> <tr> <th scope="row">sheet_length</th> <td>integer</td> <td>input data. Facultative. The height of the nesting area. Given in m<sup>-4</sup></td> </tr> <tr> <th scope="row">sheet_type</th> <td>integer</td> <td>input data. Kind of the nesting.</td> </tr> <tr> <th scope="row">tasks_index</th> <td>integer</td> <td>Generated data. Join key between tables.</td> </tr> <tr> <th scope="row">is_train, is_val, is_test</th> <td>boolean</td> <td>Generated data. Can be used as mask for the train, val and test subsets.</td> </tr> </tbody> </table> <p><em>Parts.gz</em>&nbsp;contains description of the parts to be nested :&nbsp;</p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><strong>tasks_index</strong></td> <td>integer</td> <td>Reference to the join key from the <em>Task</em> table.&nbsp;</td> </tr> <tr> <td><strong>parts_id</strong></td> <td>integer</td> <td>Generated part id.</td> </tr> <tr> <td><strong>shape_hash</strong></td> <td>integer</td> <td>Reference to the hash of the part&#39;s shape, join key from the <em>Shape </em>table.</td> </tr> </tbody> </table> <p><em>Shapes.gz</em> is the description of the shapes of the parts to be nested :&nbsp;</p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><strong>shape_hash</strong></td> <td>integer</td> <td>Generated data. Join key between tables.</td> </tr> <tr> <td><strong>raw</strong></td> <td>list of integers</td> <td>List of x, y tuples for each point. Unit is m<sup>-4</sup></td> </tr> <tr> <td><strong>sizes</strong></td> <td>list of integers</td> <td>List of sub-shapes sizes.&nbsp;</td> </tr> </tbody> </table> <p><em>Constraints.gz</em> describes&nbsp;constraints and their parameters:</p> <table> <thead> <tr> <th scope="row">Column</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><strong>type</strong></td> <td>string</td> <td>Generated constraint type.</td> </tr> <tr> <td><strong>tasks_index</strong></td> <td>integer</td> <td>Reference to the join key from the <em>Task</em> table.</td> </tr> <tr> <td><strong>parts_1, parts_2</strong></td> <td>list of integers</td> <td>References to the <em>parts_id</em> from the <em>Parts</em> table.&nbsp;&nbsp;</td> </tr> <tr> <td><strong>p1_x, p1_y and p2_x, p2_y</strong></td> <td>list of floats</td> <td>Input data. Origin position (x, y) of the constraint on parts. For each part of the constraint.</td> </tr> <tr> <td><strong>r1_start, r1_end, r1_flip_x</strong></td> <td>list of floats</td> <td>Input data. Rotation (start, end, and flip_x) parameters of the constraint. Multiple ranges accepted.</td> </tr> <tr> <td><strong>y_min, y_max</strong></td> <td>list of floats</td> <td>Input data. Range from (y_min, y_max). Multiple ranges accepted.</td> </tr> <tr> <td> <p><strong>x_offset, y_offset, motif_order, x_alignment_type, y_alignment_type, proximity_type, max_distance, groups_relative_orientation, is_frozen</strong></p> </td> <td>float</td> <td>Input data. Other constraint parameters.</td> </tr> </tbody> </table>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
8
Engagement
4

Topics