scipy
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.
As of version 0.22-0.3.0, most classes and functions from scikit-learn
and Numpy should be usable. Many examples have been ported from Python
to OCaml successfully (see below). However, the APIs have not yet
proved stable and will probably evolve in the next releases.
Example : support vector regression with RBF kernel
module Np = Np.Numpy
let n_samples, n_features = 10, 5 in
Np.Random.seed 0;
let y = Np.Random.uniform ~size:[n_samples] () in
let x = Np.Random.uniform ~size:[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 ~y;
Format.printf "%a\n" Np.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 inexamples/auto
,
for instanceexamples/auto/svm.ml
.
Installation
opam install sklearn
Finding Python's scikit-learn at runtime
You do not need a Python installation when compiling your OCaml
program using ocaml-sklearn. However, when running, your program will
need to load the sklearn, numpy and scipy Python libraries, so these
must be installed where the OCaml program is deployed.
A version of ocaml-sklearn is tied to a version of Python's
scikit-learn, numpy and scipy. 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 however be
0.22.1, 0.22.2 or 0.22.2.post1).
One way to make sure you run with the right versions is to create a
virtualenv, install scikit-learn the Python packages inside, and run
your OCaml program in the activated virtualenv.
Do this once to create the virtualenv in directory .venv
and install
scikit-learn, numpy and scipy inside:
python3 -mvenv .venv
source .venv/bin/activate
pip install scikit-learn==%%SKLEARN_FULL_VERSION%% numpy==%%NUMPY_FULL_VERSION%% scipy==%%SCIPY_FULL_VERSION%% pytest
Then run your compiled OCaml program inside the virtualenv:
source .venv/bin/activate
./my_ocaml_program.exe
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 moduleSklearn.Svm.SVC
. This module has a create
function to construct anSVC
and functions corresponding to the Python methods and
attributes.
Most data is passed in and out of sklearn through module Ndarray
(in
module Np.Np.Numpy
).
You should generally build a dense array using the constructors in Np.Numpy
:
module Np = Np.Numpy
let x = Np.matrixi [|[| 1; 2 |]; [| 3; 4 |]|]
To get data out of an Ndarray
, use to_int_array
, to_float_array
or to_string_array
(all of these return a flattened copy of the
data, and will raise an exception if the data type is wrong).
Attributes are exposed read-only, each with two getters: one that
raises Not_found if the attribute is None, and the other that returns
an option.
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
(calledto_pyobject
), so that you can always escape and use pyml
directly
if the API provided here is incomplete.
No attempt is made to expose features marked as deprecated.
Development notes
ocaml-sklearn's sources are generated using a Python program (seelib/skdoc.py
) that loads up sklearn and uses introspection to
generate bindings based on pyml
. To determine types, it parses
scikit-learn's documentation.
Dev tl;dr
python3 -mvenv .venv
source .venv/bin/activate
pip install -r requirements-dev.txt
opam switch create . 4.11.1 --deps-only
dune runtest
Python requirements
The requirements for developing (not using) the bindings are in filerequirements-dev.txt
. Install it using:
# sudo apt install python3-venv
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.
The following examples have been ported completely:
The following examples still need to be ported:
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.
sha256=48809d88893a3f17d79f8e5acbd28126de919b8ced6d1f6856a61fd6bfae571d
sha512=9e1d01c42aed436163b1ce50bee141f40cb5bc943d5dd16d6eb21f1b53d613933533c70f28675e418a550cf44e0cd66d47496e462132769b05dec64bf3db560c
>= "sk0.23-0.3.1"