Add-on containing scoring engine widgets for PFA, PMML and ONNX (coming soon) models
To use PMML models make sure you have Java installed:
- Java >= 1.8
- pypmml (downloaded during installation)
To use PFA models:
- titus2 (downloaded during installation)
To install the add-on using pip, run
pip install orange3-scoring
To register this add-on with Orange, but keep the code in the development directory (do not copy it to Python's site-packages directory), run
pip install -e .
Issues, Questions and Feature Requests
Please raise an issue/question/request here.
Want to contribute? Great!
After the installation, the widget from this add-on is registered with Orange. To run Orange from the terminal, use
python -m Orange.canvas
The new widget appears in the toolbox bar under the section
Drag and drop the
Load PMML/PFA Model widget.
Load your PMML model and inspect Input and Output field(s). Sample PMML File here.
Add input dataset using
File widget (iris) and connect the two widgets to
Evaluate PMML/PFA Model widget. You can inspect the fields in data and the model and view Processing INFO or Errors.
Score button to score.
Connect the output to
Data Table widget to view the results. 3 new columns (cluster, cluster_name & distance) are added after scoring the data obtained for each input record. The actual class value present in the data is also converted to metadata of the result table.
Now lets load a PFA Model. Sample PFA File here.
Score the data using new PFA Model.
Score button to score.
View the results. You can see the predicted class for iris as provided by the PFA Model.
Another output signal is produced which contains the
Evaluation Results which can be connected to
ROC Analysis and
Lift Curve widgets. We can connect it to the
Confusion Matrix widget to view the difference in predicted and actual results.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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