package sklearn

scikit-learn for OCaml, version %%OCAML_SKLEARN_FULL_VERSION%%

ocaml-sklearn allows using Python's scikit-learn machine learning library from OCaml.

Read the online scikit-learn OCaml API documentation here.

If you are not familiar with scikit-learn, consult its Python getting started documentation and user guide.

The current OCaml API is not complete. Some functions may be hard or impossible to use. Also, the existing API is not stable, it may change to accomodate more functionality or make things easier to use.

Example : support vector regresion with RBF kernel

let n_samples, n_features = 10, 5 in
Random.init 0;
let y = Sklearn.Ndarray.of_bigarray @@ Owl.Arr.uniform [|n_samples|] in
let x = Sklearn.Ndarray.of_bigarray @@ Owl.Dense.Matrix.D.uniform n_samples n_features in
let open Sklearn.Svm in
let clf = SVR.create ~c:1.0 ~epsilon:0.2 () in
Format.printf "%a\n" SVR.pp @@ SVR.fit clf ~x:(`Ndarray x) ~y;
Format.printf "%a\n" Sklearn.Ndarray.pp @@ SVR.support_vectors_ clf;;

This outputs:

SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma='scale',
    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
[[0.14509922 0.16277752 0.99033894 0.84013554 0.96508279]
 [0.8865312  0.80655193 0.07459775 0.36058768 0.22130337]
 [0.21844203 0.09612442 0.49908686 0.1154579  0.98202969]
 [0.07306658 0.97225754 0.20558949 0.16423512 0.57400651]
 [0.08153976 0.41462111 0.66190418 0.70208221 0.3600998 ]
 [0.20502873 0.04244781 0.21800856 0.28184598 0.4282653 ]
 [0.89211037 0.51466381 0.23432621 0.29850877 0.13323457]]

There are more examples in examples/auto, for instance examples/auto/svm.ml.

Installation

(TODO, need to publish on opam)

Finding Python's scikit-learn at runtime

At runtime, ocaml-sklearn expects to load the right version of Python's scikit-learn. One way to do that is to create a virtualenv, install scikit-learn version %%SKLEARN_FULL_VERSION%% inside, and run your OCaml program in the activated virtualenv.

Do this once to create the virtualenv in .venv and install scikit-learn inside:

python3 -mvenv .venv
source .venv/bin/activate
pip install scikit-learn==%%SKLEARN_FULL_VERSION%% pytest

Then run your compiled OCaml program inside the virtualenv:

source .venv/bin/activate
./my_ocaml_program.exe

A version of ocaml-sklearn is tied to a version of Python's sklearn. For instance, a version of ocaml-sklearn for Python's scikit-learn 0.22.2 will refuse to initialize (by throwing an exception) if scikit-learn's version is not 0.22 (it can be 0.22.1, 0.22.2 or 0.22.2.post1).

API

We attempt to bind all of scikit-learn's APIs. However, not all of the APIs are currently tested, and some are probably hard to use or unusable at the moment.

Each Python module or class gets its own OCaml module. For instance Python class sklearn.svm.SVC can be found in OCaml module Sklearn.Svm.SVC. This module has a create function to construct an SVC and functions corresponding to the Python methods and attributes.

Most data is passed in and out of sklearn through a binding to Numpy's Ndarray. This is a tensor type that can contain integers, floats, or strings (as exposed in the current OCaml API). The way to get data into or out of an Ndarray.t is to go through OCaml arrays or bigarrays. A way to create bigarrays is to use Owl's facilities. At the moment, the Ndarray functions exposed here are extremely minimal (no np.zeros or np.ones).

Bunches (as returned from the sklearn.datasets APIs) are exposed as objects.

Arguments taking string values are converted (in most cases) to polymorphic variants.

Each module has a conversion function to Py.Object.t, so that you can always escape and use pyml directly if the API provided here is incomplete.

Development notes

ocaml-sklearn's sources are generated using a Python program (see lib/skdoc.py) that loads up sklearn and uses introspection to generate bindings based on pyml. To determine types, it parses scikit-learn's documentation.

Python requirements

The requirements for developing (not using) the bindings are in file requirements-dev.txt. Install it using:

python3 -mvenv .venv
source .venv/bin/activate
pip install -r requirements-dev.txt

Running tests

dune runtest

The tests are in examples/auto. They are based on examples extracted from the Python documentation. A good way to develop is to pick one of the files and start porting examples. One can refer to examples/auto/svm.ml or examples/auto/pipeline.ml, whose examples have already been ported (almost) completely.

Generating documentation

lib/build-doc

Documentation can then be found in html_doc/. Serve it locally with something like:

python3 -mhttp.server --directory html_doc
xdg-open http://localhost:8000

License

BSD-3. See file LICENSE.