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<h1 id="interfacing-hyperopt-with-other-programming-languages">Interfacing Hyperopt with other programming languages</h1>
<p>There are basically two ways to interface hyperopt with other languages:</p>
<ol>
<li>you can write a Python wrapper around your cost function that is not written in Python, or</li>
<li>you can replace the <code>hyperopt-mongo-worker</code> program and communicate with MongoDB directly using JSON.</li>
</ol>
<h2 id="wrapping-a-call-to-non-python-code">Wrapping a call to non-Python code</h2>
<p>The easiest way to use hyperopt to optimize the arguments to a non-python function, such as for example an external executable, is to write a Python function wrapper around that external executable. Supposing you have an executable <code>foo</code> that takes an integer command-line argument <code>--n</code> and prints out a score, you might wrap it like this:</p>
<pre><code class="python">import subprocess
def foo_wrapper(n):
# Optional: write out a script for the external executable
# (we just call foo with the argument proposed by hyperopt)
proc = subprocess.Popen(['foo', '--n', n], stdout=subprocess.PIPE)
proc_out, proc_err = proc.communicate()
# <you might have to do some more elaborate parsing of foo's output here>
score = float(proc_out)
return score
</code></pre>
<p>Of course, to optimize the <code>n</code> argument to <code>foo</code> you also need to call hyperopt.fmin, and define the search space. I can only imagine that you will want to do this part in Python.</p>
<pre><code class="python">from hyperopt import fmin, hp, random
best_n = fmin(foo_wrapper, hp.quniform('n', 1, 100, 1), algo=random.suggest)
print best_n
</code></pre>
<p>When the search space is larger than the simple one here, you might want or need the wrapper function to translate its argument into some kind of configuration file/script for the external executable.</p>
<p>This approach is perfectly compatible with MongoTrials.</p>
<h2 id="communicating-with-mongodb-directly">Communicating with MongoDB Directly</h2>
<p>It is possible to interface more directly with the search process (when using MongoTrials) by communicating with MongoDB directly, just like <code>hyperopt-mongo-worker</code> does. It's beyond the scope of a tutorial to explain how to do this, but Hannes Schultz (@temporaer) got hyperopt working with his MDBQ project, which is a standalone mongodb-based task queue:</p>
<p><a href="https://github.com/temporaer/MDBQ/blob/master/src/example/hyperopt_client.cpp">Hyperopt C++ Client</a></p>
<p>Have a look at that code, as well as the contents of <a href="https://github.com/jaberg/hyperopt/blob/master/hyperopt/mongoexp.py">hyperopt/mongoexp.py</a> to understand how worker processes are expected to reserve jobs in the work queue, and store results back to MongoDB.</p></div>
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