title: How to configurate hyperparater optimization in ray via toml
body: <p>is there a way to initialize the following completely within a toml-file without using a vast amount of if-cases?</p>
  <p>Note that the optimal way would be to use precisely the notion below as input and get the same output if would read the variable parm_dists from that toml-file.</p>
  <pre><code>param_dists = {
      'loss': tune.choice(['squared_hinge', 'hinge']),
      'alpha': tune.loguniform(1e-4, 1e-1),
      'epsilon': tune.uniform(1e-2, 1e-1),
  }
  </code></pre>
  <p><strong>Context:</strong> That question comes up while implementing an ml-framework for a technical user without a machine learning background.
  The primary goal is to enable those users to work with ml on the company's massive data to make it as easy as possible.
  The framework uses ray for hyperparameter tuning and focuses on sklearn model right now.</p>
  <p>A solution with if-clauses looks horrifying from a clean-code perspective. Furthermore, it hard to maintain.  Image, we have three models, with a minimum of 2 hyperparameters each and four sampling algorithms that make sense in this Context. Hence, this leads to many false cases.</p>
  <p><em>However, if no one can think of a particular solution, a couple of tips and tricks on how to deal with toml-files would be great.</em></p>