Consider improving the parameter configuration interface

[ili3p]

Currently the parameter configuration is being set in this way:

        self.xlow = -15 * np.ones(dim)
        self.xup = 20 * np.ones(dim)
        self.integer = []
        self.continuous = np.arange(0, dim)

This is very cumbersome interface, especially when you have 15 parameters, each with different ranges, and are of different type. It's very easy to make a mistake.
Further if the type of parameter is left unassigned, there is no error or warning. Example

      self.integer = [0, 1, 2]
      self.continuous = [4, 5]

Here the fourth parameter is left without a type (integer or continuous), but there is no error or warning produced, and the algorithm just considers it as continuous.

Also, it would be useful if we can assign a name to a parameter.

I suggest to adopt some JSON configuration file, similar to what other optimization algorithms use. Example:

       {
           parameters:[
                      {
                          name: "learning_rate",
                          low: 10,
                          high: 20,
                          type: "Integer"
                      },
                     ...
           ]
       }

Most importantly produce an error or at least a warning if not all of the configuration properties are set.

Thanks

[dme65]

There is a method for sanity checking your optomization problem setup. The method is named validate and is available in test_problems.py. I agree that this should be checked automatically by the strategy and I'm working on adding some input checking to the strategies.

[dme65]

The optimization problem is sanity checked automatically in 0.1.20