Traditional machine learning tools built on top of Nx. Scholar implements several algorithms for classification, regression, clustering, dimensionality reduction, metrics, and preprocessing.

For deep learning, see Axon. For decision trees/forests, see EXGBoost.

Installation

Mix projects

Add to your mix.exs:

def deps do
  [
    {:scholar, "~> 0.1"}
  ]
end

Besides Scholar, you will most likely want to use an existing Nx compiler/backend, such as EXLA:

def deps do
  [
    {:scholar, "~> 0.1"},
    {:exla, ">= 0.0.0"}
  ]
end

And then in your config/config.exs file:

import Config
config :nx, :default_backend, EXLA.Backend
# Client can also be set to :cuda / :rocm
config :nx, :default_defn_options, [compiler: EXLA, client: :host]

Notebooks

To use Scholar inside code notebooks, run:

Mix.install([
  {:scholar, "~> 0.1"},
  {:exla, ">= 0.0.0"}
])

Nx.global_default_backend(EXLA.Backend)
# Client can also be set to :cuda / :romc
Nx.Defn.global_default_options(compiler: EXLA, client: :host)

License

Copyright (c) 2022 The Machine Learning Working Group of the Erlang Ecosystem Foundation

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.