Posted on July 11, 2016

*Note: This post is old, and discusses an experimental library that no longer
exists. Please see this post on dask-searchcv,
and the corresponding
documentation for the current
state of things.*

This is the first of a series of posts discussing some recent experiments combining dask and scikit-learn. A small (and extremely alpha) library has been built up from these experiments, and can be found here.

Before we start, I would like to make the following caveats:

- I am not a machine learning expert. Do not consider this a guide on how to do machine learning, the usage of scikit-learn below is probably naive.
- All of the code discussed here is in flux, and shouldn't be considered stable or robust. That said, if you know something about machine learning and want to help out, I'd be more than happy to receive issues or pull requests :).

There are several ways of parallelizing algorithms in machine learning. Some algorithms can be made to be data-parallel (either across features or across samples). In this post we'll look instead at model-parallelism (use same data across different models), and dive into a daskified implementation of GridSearchCV.

Many machine learning algorithms have *hyperparameters* which can be tuned to
improve the performance of the resulting estimator. A grid
search
is one way of optimizing these parameters — it works by doing a parameter
sweep across a cartesian product of a subset of these parameters (the "grid"),
and then choosing the best resulting estimator. Since this is fitting many
independent estimators across the same set of data, it can be fairly easily
parallelized.

In scikit-learn, a grid search is performed using the `GridSearchCV`

class, and
can (optionally) be automatically parallelized using
joblib.

This is best illustrated with an example. First we'll make an example dataset for doing classification against:

```
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=10000,
n_features=500,
n_classes=2,
n_redundant=250,
random_state=42)
```

To solve this classification problem, we'll create a pipeline of a `PCA`

and a
`LogisticRegression`

:

```
from sklearn import linear_model, decomposition
from sklearn.pipeline import Pipeline
logistic = linear_model.LogisticRegression()
pca = decomposition.PCA()
pipe = Pipeline(steps=[('pca', pca),
('logistic', logistic)])
```

Both of these classes take several hyperparameters, we'll do a grid-search across only a few of them:

```
#Parameters of pipelines can be set using ‘__’ separated parameter names:
grid = dict(pca__n_components=[50, 100, 250],
logistic__C=[1e-4, 1.0, 1e4],
logistic__penalty=['l1', 'l2'])
```

Finally, we can create an instance of `GridSearchCV`

, and perform the grid
search. The parameter `n_jobs=-1`

tells joblib to use as many processes as I
have cores (8).

```
from sklearn.grid_search import GridSearchCV
estimator = GridSearchCV(pipe, grid, n_jobs=-1)
%time estimator.fit(X, y)
```

CPU times: user 5.3 s, sys: 243 ms, total: 5.54 s
Wall time: 21.6 s

What happened here was:

- An estimator was created for each parameter combination and test-train set (scikit-learn's grid search also does cross validation across 3-folds by default).
- Each estimator was fit on its corresponding set of training data
- Each estimator was then scored on its corresponding set of testing data
- The best set of parameters was chosen based on these scores
- A new estimator was then fit on
*all*of the data, using the best parameters

The corresponding best score, parameters, and estimator can all be found as attributes on the resulting object:

```
estimator.best_score_
```

0.89290000000000003

```
estimator.best_params_
```

{'logistic__C': 0.0001, 'logistic__penalty': 'l2', 'pca__n_components': 50}

```
estimator.best_estimator_
```

Pipeline(steps=[('pca', PCA(copy=True, n_components=50, whiten=False)), ('logistic', LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, max_iter=100,
multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,
solver='liblinear', tol=0.0001, verbose=0, warm_start=False))])

{'logistic__C': 0.0001, 'logistic__penalty': 'l2', 'pca__n_components': 50}

## Grid search with dask-learn
Here we'll repeat the same fit using dask-learn. I've tried to match the
scikit-learn interface as much as possible, although not everything is
implemented. Here the only thing that really changes is the `GridSearchCV`
import. We don't need the `n_jobs` keyword, as this will be parallelized across
all cores by default.
::Python
from dklearn.grid_search import GridSearchCV as DaskGridSearchCV
destimator = DaskGridSearchCV(pipe, grid)
%time destimator.fit(X, y)
CPU times: user 16.3 s, sys: 1.89 s, total: 18.2 s
Wall time: 5.63 s

As before, the best score, parameters, and estimator can all be found as
attributes on the object. Here we'll just show that they're equivalent:
::Python
destimator.best_score_ == estimator.best_score_
True

::Python
destimator.best_params_ == estimator.best_params_
True

::Python
destimator.best_estimator_
Pipeline(steps=[('pca', PCA(copy=True, n_components=50, whiten=False)), ('logistic', LogisticRegression(C=0.0001, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, max_iter=100,
multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,
solver='liblinear', tol=0.0001, verbose=0, warm_start=False))])
::Python
%time destimator.fit(X, y)

{'logistic__C': 0.0001, 'logistic__penalty': 'l2', 'pca__n_components': 50}

## Why is the dask version faster?
If you look at the times above, you'll note that the dask version was `~4X`
faster than the scikit-learn version. This is not because we have optimized any
of the pieces of the `Pipeline`, or that there's a significant amount of
overhead to `joblib` (on the contrary, joblib does some pretty amazing things,
and I had to construct a contrived example to beat it this badly). The reason
is simply that the dask version is doing less work.
This maybe best explained in pseudocode. The scikit-learn version of the above
(in serial) looks something like (pseudocode):
::Python
for X_train, X_test, y_train, y_test in cv:
for n in grid['pca__n_components']:
for C in grid['logistic__C']:
for penalty in grid['logistic__penalty']:
# Create and fit a PCA on the input data
pca = PCA(n_components=n).fit(X_train, y_train)
# Transform both the train and test data
X_train2 = pca.transform(X_train)
X_test2 = pca.transform(X_test)
# Create and fit a LogisticRegression on the transformed data
logistic = LogisticRegression(C=C, penalty=penalty)
logistic.fit(X_train2, y_train)
# Score the total pipeline
score = logistic.score(X_test2, y_test)
# Save the score and parameters
scores_and_params.append((score, n, C))
# Find the best set of parameters (for some definition of best)
find_best_parameters(scores)
This is looping through a cartesian product of the cross-validation sets and
all the parameter combinations, and then creating and fitting a new estimator
for each combination. While embarassingly parallel, this can also result in
repeated work, as earlier stages in the pipeline are refit multiple times on
the same parameter + data combinations.
In contrast, the dask version hashes all inputs (forming a sort of [Merkle
DAG](https://en.wikipedia.org/wiki/Merkle_tree)), resulting in the intermediate
results being shared. Keeping with the pseudocode above, the dask version might
look like:
::Python
for X_train, X_test, y_train, y_test in cv:
for n in grid['pca__n_components']:
# Create and fit a PCA on the input data
pca = PCA(n_components=n).fit(X_train, y_train)
# Transform both the train and test data
X_train2 = pca.transform(X_train)
X_test2 = pca.transform(X_test)
for C in grid['logistic__C']:
for penalty in grid['logistic__penalty']:
# Create and fit a LogisticRegression on the transformed data
logistic = LogisticRegression(C=C, penalty=penalty)
logistic.fit(X_train2, y_train)
# Score the total pipeline
score = logistic.score(X_test2, y_test)
# Save the score and parameters
scores_and_params.append((score, n, C, penalty))
# Find the best set of parameters (for some definition of best)
find_best_parameters(scores)
This can still be parallelized, but in a less straightforward manner - the
graph is a bit more complicated than just a simple map-reduce pattern.
Thankfully the [dask
schedulers](http://dask.pydata.org/en/latest/scheduler-overview.html) are well
equipped to handle arbitrary graph topologies. Below is a GIF showing how the
dask scheduler (the threaded scheduler specifically) executed the grid search
performed above. Each rectangle represents data, and each circle represents a
task. Each is categorized by color:
- Red means actively taking up resources. These are tasks executing in a thread,
or intermediate results occupying memory
- Blue means finished or released. These are already finished tasks, or data
that's been released from memory because it's no longer needed
Looking at the trace, a few things stand out:
- We do a good job sharing intermediates. Each step in a pipeline is only fit
once given the same parameters/data, resulting in some intermediates having
many dependent tasks.
- The scheduler does a decent job of quickly finishing up tasks required to
release data. This doesn't matter as much here (none of the intermediates
take up much memory), but for other workloads this is very useful. See Matt
Rocklin's [excellent blogpost
here](http://matthewrocklin.com/blog/work/2015/01/06/Towards-OOC-Scheduling)
for more discussion on this.
## Distributed grid search using dask-learn
The [schedulers](http://dask.pydata.org/en/latest/scheduler-overview.html) used
in dask are configurable. The default (used above) is the threaded scheduler,
but we can just as easily swap it out for the distributed scheduler. Here I've
just spun up two local workers to demonstrate, but this works equally well
across multiple machines.
::Python
from distributed import Executor
# Create an Executor, and set it as the default scheduler
exc = Executor('10.0.0.3:8786', set_as_default=True)
exc
CPU times: user 1.69 s, sys: 433 ms, total: 2.12 s
Wall time: 7.66 s

::Python
(destimator.best_score_ == estimator.best_score_ and
destimator.best_params_ == estimator.best_params_)
True

Note that this is slightly slower than the threaded execution, so it doesn't
make sense for this workload, but for others it might.
## What worked well
- The [code for doing
this](https://github.com/dask/dask-learn/blob/master/dklearn/grid_search.py)
is quite short. There's also an implementation of
[`RandomizedSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.RandomizedSearchCV.html),
which is only a few extra lines (hooray for good class hierarchies!).
Instead of working with dask graphs directly, both implementations use
[dask.delayed](http://dask.pydata.org/en/latest/delayed.html) wherever
possible, which also makes the code easy to read.
- Due to the internal hashing used in dask (which is extensible!), duplicate
computations are avoided.
- Since the graphs are separated from the scheduler, this works both locally
and distributed with only a few extra lines.
## Caveats and what could be better
- The scikit-learn api makes use of mutation (`est.fit(X, y)` mutates `est`),
while dask collections are mostly immutable. After playing around with a few
different ideas, I settled on dask-learn estimators being immutable (except
for grid-search, more on this in a bit). This made the code easier to reason
about, but does mean that you need to do `est = est.fit(X, y)` when working
with dask-learn estimators.
- `GridSearchCV` posed a different problem. Due to the `refit` keyword, the
implementation can't be done in a single pass over the data. This means that
we can't build a single graph describing both the grid search and the refit,
which prevents it from being done lazily. I debated removing this keyword,
but decided in the end to make `fit` execute immediately. This means that
there's a bit of a disconnect between `GridSearchCV` and the other classes in
the library, which I don't like. On the other hand, it does mean that this
version of `GridSearchCV` could be a drop-in for the sckit-learn one.
- The approach presented here is nice, but is really *only beneficial when
there's duplicate work to be avoided, and that duplicate work is expensive*.
Repeating the above with only a single estimator (instead of a pipeline)
results in identical (or slightly worse) performance than joblib. Similarly,
if the repeated steps are cheap the difference in performance is much smaller
(try the above using
[SelectKBest](http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html)
instead of `PCA`).
- The ability to swap easily from local to distributed execution is nice, but
[distributed also contains a joblib
frontend](http://distributed.readthedocs.io/en/latest/joblib.html) that can
do this just as easily.
## Help
I am not a machine learning expert. Is any of this useful? Do you have
suggestions for improvements (or better yet PRs for improvements :))? Please
feel free to reach out in the comments below, or [on
github](https://github.com/dask/dask-learn).
*This work is supported by [Continuum Analytics](http://continuum.io/) and the
[XDATA](http://www.darpa.mil/program/XDATA) program as part of the [Blaze
Project](http://blaze.pydata.org/).*