# The scikit-learn-speed ship has set sail! Faster than ever, with multi-step benchmarks!

I am pleased to announce that last night at 2:03 AM, the first fully automated run of the scikit-learn-speed test suite has run on our Jenkins instance! You can admire it at its temporary home for now. As soon as we verify that everything is good, we will move this to the official scikit-learn page.

I would like to take this opportunity to tell you about our latest changeset. We made running the benchmark suite tons simpler by adding a friendly Makefile. You can read more about its usage in the guide. But by far, our coolest new toy is:

## Multi-step benchmarks¶

A standard vbench benchmark has three units of code, represented as strings: code, setup and cleanup. With the original timeit-based benchmarks, this means that for every run, the setup would be executed once. Then, the main loop runs repeat times, and within each iteration, the code is run ncalls times. Then cleanup happens, the best time is returned, and everybody is happy.

In scikit-learn, most of our interesting objects go through a state change called fitting. This metaphor is right at home in the machine learning field, where we separate the learning phase for the prediction phase. The prediction step cannot be invoked on an object that hasn’t been fitted.

For some algorithms, one of these steps is trivial. A brute force Nearest Neighbors classifier can be instantaneously fit, but prediction takes a while. On the opposite end we have linear models, with tons of complicated algorithms to fit them, but evaluation is a simple matrix-vector product that Numpy handles perfectly.

But many of scikit-learn’s estimators have both steps interesting. Let’s take Non-negative Matrix Factorization. It has three interesting functions: The fit that computes $latex X = WH$, the transform that computes a non-negative projection on the components learned in fit, and fit_transform that takes advantage of the observation that when fitting, we also get the transformed $latex X$ for free.

When benchmarking NMF, we initially had to design 3 benchmarks:

• setup =standard, code = obj.fit(X)
• setup =standard, code = obj.fit_transform(X)
• setup =standard+ obj.fit(X), code = obj.transform(X)

## How much time were we wasting?¶

Let’s say it takes 10 seconds. For every benchmark, we time the code by running it 3 times. We run it once more to measure memory usage, once more for cProfile and one last time for line_profiler. This is a total of 6 times per benchmark. We need to multiply this by 2 again for running on two datasets. So when benchmarking NMF, because we need to fit before predicting, we do it 12 extra times. If a fit takes 5 seconds, this means one minute wasted on benchmarking just one estimator. Wouldn’t it be nice to fit, fit_transform and transform in a sequence?

## Behind the scenes¶

We made the PythonBenchmark code parameter also support getting a sequence of strings, instead of just a string. On the database side, every benchmark result entry gets an extra component in the primary key, the number of the step it measures.

In the benchmark description files, nothing is changed:

[sourcecode lang=”python”]
{
‘obj’: ‘NMF’,
‘init_params’: {‘n_components’: 2},
‘datasets’: (‘blobs’,),
‘statements’: (‘fit_unsup’, ‘transform_unsup’, ‘fit_transform’)
},
[/sourcecode]

But before, we would take the cartesian product of datasets and statements, and build a Benchmark object for every pairing. Now, we just pass the tuple as it is, and vbench is smart enough to do the right thing.
We avoided the extra calls to fit in a lot of benchmarks. The whole suite now takes almost half the time to run!

Note: This trick is currently hosted in the abstract_multistep_benchmarks vbench branch in my fork.