package np

Fundamental scientific computing with Numpy for OCaml

Sources

sklearn-v0.3.1.tbz
`sha256=48809d88893a3f17d79f8e5acbd28126de919b8ced6d1f6856a61fd6bfae571d`
`sha512=9e1d01c42aed436163b1ce50bee141f40cb5bc943d5dd16d6eb21f1b53d613933533c70f28675e418a550cf44e0cd66d47496e462132769b05dec64bf3db560c`

scikit-learn for OCaml

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 in `examples/auto`, for instance `examples/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 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 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` (called `to_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 (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.

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 file `requirements-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
``````