Difference between revisions of "Orange: Pythagorean Tree"

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Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/pythagoreantree.html
 
Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/pythagoreantree.html
  
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Widget Pythagorean Tree dapat mem-visualisasi untuk klasifikasi atau regression tree.
  
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Pythagorean tree visualization for classification or regression trees.
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==Input==
  
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Inputs
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Tree: tree model
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Selected Data: instances selected from the tree
  
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    Tree: tree model
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Pythagorean Tree adalah bidang fraktal yang dapat digunakan untuk menggambarkan hierarki tree umum seperti yang disajikan dalam sebuah artikel oleh Fabian Beck dkk. Di Orange, widget Pythagorean Tree digunakan untuk memvisualisasikan dan menjelajahi model tree.
  
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    Selected Data: instances selected from the tree
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[[File:Pythagorean-Tree1-stamped.png|center|600px|thumb]]
  
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Pythagorean Trees are plane fractals that can be used to depict general tree hierarchies as presented in an article by Fabian Beck and co-authors. In our case, they are used for visualizing and exploring tree models, such as Tree.
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* Information on the input tree model.
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* Visualization parameters:
  
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../../_images/Pythagorean-Tree1-stamped.png
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** Depth: set the depth of displayed trees.
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** Target class (for classification trees): the intensity of the color for nodes of the tree will correspond to the probability of the target class. If None is selected, the color of the node will denote the most probable class.
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** Node color (for regression trees): node colors can correspond to mean or standard deviation of class value of the training data instances in the node.
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** Size: define a method to compute the size of the square representing the node. Normal will keep node sizes correspond to the size of training data subset in the node. Square root and Logarithmic are the respective transformations of the node size.
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** Log scale factor is only enabled when logarithmic transformation is selected. You can set the log factor between 1 and 10.
  
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    Information on the input tree model.
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* Plot properties:
  
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    Visualization parameters:
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** Enable tooltips: display node information upon hovering.
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** Show legend: shows color legend for the plot.
  
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        Depth: set the depth of displayed trees.
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* Reporting:
  
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        Target class (for classification trees): the intensity of the color for nodes of the tree will correspond to the probability of the target class. If None is selected, the color of the node will denote the most probable class.
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** Save Image: save the visualization to a SVG or PNG file.
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** Report: add visualization to the report.
  
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        Node color (for regression trees): node colors can correspond to mean or standard deviation of class value of the training data instances in the node.
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Widget Pythagorean Tree dapat memvisualisasikan classification dan regression tree. Di bawah ini adalah contoh untuk  regression tree. Satu-satunya perbedaan antara keduanya adalah bahwa regression tree tidak memungkinkan pewarnaan berdasarkan class, tetapi dapat mewarnai dengan rata-rata class atau standar deviasi.
  
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        Size: define a method to compute the size of the square representing the node. Normal will keep node sizes correspond to the size of training data subset in the node. Square root and Logarithmic are the respective transformations of the node size.
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[[File:Pythagorean-Tree1-continuous.png|center|600px|thumb]]
  
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        Log scale factor is only enabled when logarithmic transformation is selected. You can set the log factor between 1 and 10.
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==Contoh==
  
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    Plot properties:
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Workflow dari screenshot  di bawah ini menunjukkan perbedaan antara widget Tree Viewer and widget Pythagorean Tree. Keduanya dapat memvisualisasikan Tree, tetapi visualisasi widget Pythagorean Tree membutuhkan lebih sedikit ruang dan lebih kompak, bahkan untuk Iris flower dataset yang kecil. Untuk kedua widget visualisasi, kita telah menyembunyikan area kontrol di sebelah kiri dengan mengklik splitter antara area kontrol dan visualisasi.
  
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        Enable tooltips: display node information upon hovering.
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[[File:Pythagorean-Tree-comparison.png|center|600px|thumb]]
  
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        Show legend: shows color legend for the plot.
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Widget Pythagorean Tree bersifat interaktif: klik pada salah satu node (kotak) untuk memilih contoh data pelatihan yang dikaitkan dengan node tersebut. Workflow berikut mengeksplorasi fitur ini.
  
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    Reporting:
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[[File:Pythagorean-Tree-scatterplot-workflow.png|center|400px|thumb]]
  
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        Save Image: save the visualization to a SVG or PNG file.
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Instance data yang dipilih ditampilkan sebagai subset dalam widget Scatter Plot, dikirim ke widget Data Table dan diperiksa dalam widget Box Plot. Kita telah menggunakan dataset yang dipilih berwarna cokelat dalam contoh ini. Widget Tree dan widget Scatter Plot ditunjukkan di bawah ini; node yang dipilih di tree memiliki garis hitam.
  
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        Report: add visualization to the report.
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[[File:Pythagorean-Tree-scatterplot.png|center|600px|thumb]]
  
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Pythagorean Tree can visualize both classification and regression trees. Below is an example for regression tree. The only difference between the two is that regression tree doesn’t enable coloring by class, but can color by class mean or standard deviation.
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==Referensi==
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../../_images/Pythagorean-Tree1-continuous.png
 
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Example
 
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The workflow from the screenshot below demonstrates the difference between Tree Viewer and Pythagorean Tree. They can both visualize Tree, but Pythagorean visualization takes less space and is more compact, even for a small Iris flower dataset. For both visualization widgets, we have hidden the control area on the left by clicking on the splitter between control and visualization area.
 
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../../_images/Pythagorean-Tree-comparison.png
 
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Pythagorean Tree is interactive: click on any of the nodes (squares) to select training data instances that were associated with that node. The following workflow explores these feature.
 
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../../_images/Pythagorean-Tree-scatterplot-workflow.png
 
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The selected data instances are shown as a subset in the Scatter Plot, sent to the Data Table and examined in the Box Plot. We have used brown-selected dataset in this example. The tree and scatter plot are shown below; the selected node in the tree has a black outline.
 
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../../_images/Pythagorean-Tree-scatterplot.png
 
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References
 
  
 
Beck, F., Burch, M., Munz, T., Di Silvestro, L. and Weiskopf, D. (2014). Generalized Pythagoras Trees for Visualizing Hierarchies. In IVAPP ‘14 Proceedings of the 5th International Conference on Information Visualization Theory and Applications, 17-28.
 
Beck, F., Burch, M., Munz, T., Di Silvestro, L. and Weiskopf, D. (2014). Generalized Pythagoras Trees for Visualizing Hierarchies. In IVAPP ‘14 Proceedings of the 5th International Conference on Information Visualization Theory and Applications, 17-28.

Latest revision as of 11:24, 8 April 2020

Sumber: https://docs.biolab.si//3/visual-programming/widgets/visualize/pythagoreantree.html

Widget Pythagorean Tree dapat mem-visualisasi untuk klasifikasi atau regression tree.

Input

Tree: tree model
Selected Data: instances selected from the tree

Pythagorean Tree adalah bidang fraktal yang dapat digunakan untuk menggambarkan hierarki tree umum seperti yang disajikan dalam sebuah artikel oleh Fabian Beck dkk. Di Orange, widget Pythagorean Tree digunakan untuk memvisualisasikan dan menjelajahi model tree.

Pythagorean-Tree1-stamped.png
  • Information on the input tree model.
  • Visualization parameters:
    • Depth: set the depth of displayed trees.
    • Target class (for classification trees): the intensity of the color for nodes of the tree will correspond to the probability of the target class. If None is selected, the color of the node will denote the most probable class.
    • Node color (for regression trees): node colors can correspond to mean or standard deviation of class value of the training data instances in the node.
    • Size: define a method to compute the size of the square representing the node. Normal will keep node sizes correspond to the size of training data subset in the node. Square root and Logarithmic are the respective transformations of the node size.
    • Log scale factor is only enabled when logarithmic transformation is selected. You can set the log factor between 1 and 10.
  • Plot properties:
    • Enable tooltips: display node information upon hovering.
    • Show legend: shows color legend for the plot.
  • Reporting:
    • Save Image: save the visualization to a SVG or PNG file.
    • Report: add visualization to the report.

Widget Pythagorean Tree dapat memvisualisasikan classification dan regression tree. Di bawah ini adalah contoh untuk regression tree. Satu-satunya perbedaan antara keduanya adalah bahwa regression tree tidak memungkinkan pewarnaan berdasarkan class, tetapi dapat mewarnai dengan rata-rata class atau standar deviasi.

Pythagorean-Tree1-continuous.png

Contoh

Workflow dari screenshot di bawah ini menunjukkan perbedaan antara widget Tree Viewer and widget Pythagorean Tree. Keduanya dapat memvisualisasikan Tree, tetapi visualisasi widget Pythagorean Tree membutuhkan lebih sedikit ruang dan lebih kompak, bahkan untuk Iris flower dataset yang kecil. Untuk kedua widget visualisasi, kita telah menyembunyikan area kontrol di sebelah kiri dengan mengklik splitter antara area kontrol dan visualisasi.

Pythagorean-Tree-comparison.png

Widget Pythagorean Tree bersifat interaktif: klik pada salah satu node (kotak) untuk memilih contoh data pelatihan yang dikaitkan dengan node tersebut. Workflow berikut mengeksplorasi fitur ini.

Pythagorean-Tree-scatterplot-workflow.png

Instance data yang dipilih ditampilkan sebagai subset dalam widget Scatter Plot, dikirim ke widget Data Table dan diperiksa dalam widget Box Plot. Kita telah menggunakan dataset yang dipilih berwarna cokelat dalam contoh ini. Widget Tree dan widget Scatter Plot ditunjukkan di bawah ini; node yang dipilih di tree memiliki garis hitam.

Pythagorean-Tree-scatterplot.png

Referensi

Beck, F., Burch, M., Munz, T., Di Silvestro, L. and Weiskopf, D. (2014). Generalized Pythagoras Trees for Visualizing Hierarchies. In IVAPP ‘14 Proceedings of the 5th International Conference on Information Visualization Theory and Applications, 17-28.



Referensi

Pranala Menarik

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