# Introduction

randopt is a python package that will help you manage and run experiments. On top of that, it provides functionalities for hyper-parameter search. It's still in its early days, and we are working on adding more features while keeping the current streamlined workflow.

# Installation

No dependencies. To install randopt execute

pip install randopt

Code sources are also available on the GitHub repo

https://github.com/seba-1511/randopt/

# Overview

randopt provides two main utilities: a programmatic interface to experiments and a visualization tool. Here's what a typical randopt workflow looks like.

1. Annotate your experiments with randopt.Experiment,
2. Run that same script so as to try multiple configurations,
3. Analyse the results using roviz.py or the programmatic API.

### Simple Example

TL;DR: Check out this example to get started.

Let's work through a simple example; trying to find the minimum value of $$x^2$$ by random search. We first define our loss function, which will return the value we're trying to optimize for.

            def loss(x):
return x**2

Our example is trivial, and a real-world application will more likely to look like minimizing the validation accuracy of a learning algorithm.

Now that we have a loss function we create an experiment using randopt.Experiment(name, params).

            import randopt as ro
experiment = ro.Experiment('simple_example', {
'alpha': ro.Gaussian(mean=0.0, std=0.5)
})

In the above snippet, 'simple_example' is the name of the experiment, and alpha is the parameter we will sample. Note

1. you could have used any name (eg, param_name) instead of alpha except for result,
2. you can define any number of parameters, and
3. once sampled, their values are accessible at experiment.param_name.

Finally, we decided to sample the value of alpha according to a Gaussian distribution, with mean 0 and standard deviation 0.5. randopt provides several distributions, and it is easy to also add you own.

It is now time for us to sample, run our experiment, and save the result.

            for i in range(100):
# Could use experiment.sample_all_params()
experiment.sample('alpha')
result = loss(experiment.alpha)
experiment.add_result(result)

Here we decided to run our experiment a 100 times. As noted in the comment we could have used experiment.sample_all_params() instead of experiment.sample('alpha'). This is particularly useful when dealing with a large number of hyper-parameters.

The crucial line is the call to experiment.add_result(result). It will save a JSON file with with the current hyper-parameters and the obtained result. You can also choose to add more information in the JSON dump by passing a dictionary as the second parameter to add_result. For example, you could pass the list of validation errors of your iterative training algorithm.

experiment.add_result(loss, data={'convergence': [10, 9, 6, 5, 3, 2, 1, 0.1]})

### Retrieving Results

There are two preferred ways to retrieve results from randopt. The first one is using roviz.py, which will generate a web-page with a summary of all results.

roviz.py simple_example

The an example of the web page generated by the above line is available here. Try clicking on the values of alpha !

The second option is to explore your results using the programmatic API. The following snippet demonstrates some of the available options to search through your results.

            mini = experiment.minimum() # Returns the exp. with minimum result
print 'Minimum result: ', mini.value, ', with params: ', mini.params

maxi = experiment.maximum() # Returns the exp. with maximum result
print 'Maximum result: ', maxi.value, ', with params: ', maxi.params

leq = lambda x, y: x <= y
top = experiment.top(3, fn=leq) # Best n according to fn. Default: leq

Of course, you can easily access each run individually through its JSON dump, which is available in the randopt_result/simple_example/ folder.

One of the advantage of dumping each result in its own file is that we can trivially parallelize the search. Hence if running the above example involves calling

python simple.py

then running that command twice, concurrently (see below for Unix) will launch two processes that should be allocated different cores by the operating system.

python simple.py & python simple.py

Even better, share your code with friends, let them run it, and then copy their JSON files in your randopt_results/simple_experiment/ folder. The search / visualization scripts will consider the entirety of the directory and your friends' results will be included too !

### What's next

randopt includes other useful utilities to manage your experiments. (eg, seed setting, experiment reproduction, fancy samplers) To learn more about them you can

Here's a list of functionalities we would like to add to randopt

• More samplers
• HyperBand support (currently experimental)
• Bayesian optimization support, especially for later stages fine-tuning
• Improved performance with JSON file management
• Fancier HTML visualization - in particular w.r.t. to plotting result, confidence intervals, etc...

# Contributors

The following people have made contributions to randopt

• Séb Arnold
• Noel Trivedi
• Cyrus Jia