We should be able to support different kinds of dynamic calculations on the trained model.
At the moment some of these operations are exposed as methods on the model found inside the result object.
One example is the exploration of the model's representation of a single point in https://github.com/novonordisk-research/ProcessOptimizer/blob/develop/examples/7Dimensional_optimization_and_plotting.ipynb where the following code is executed to generate further data
samples_of_y = res.models[-1].sample_y(transformed_point, n_samples=25000, random_state=None)`
These calculations has to be made on the server as it is the only place we have access to python. The solution currently pursued is to send the pickled result from #6 to the server and use it for further calculations
We should be able to support different kinds of dynamic calculations on the trained model.
At the moment some of these operations are exposed as methods on the model found inside the result object.
One example is the exploration of the model's representation of a single point in https://github.com/novonordisk-research/ProcessOptimizer/blob/develop/examples/7Dimensional_optimization_and_plotting.ipynb where the following code is executed to generate further data
These calculations has to be made on the server as it is the only place we have access to python. The solution currently pursued is to send the pickled result from #6 to the server and use it for further calculations