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Crystal-ML

A classic machine learning library for Crystal programming language, inspired by scikit-learn.

Crystal-ML focuses on simplicity and ease of use, accepting Array, Tensor (Num.cr) and/or DataFrame(Crystal-DA) objects as inputs in all its algorithms. All the calculations rely on Tensor operations, enabling efficient computation and ─future─ support for GPU operations.

Installation

Add this to your application's shard.yml:

dependencies:
  crystal-ml:
    github: manastech/crystal-ml

Then, run shards install.

Usage

Let's take as an example the KMeans algorithm, that partitions data into K distinct clusters based on distance to the centroid of each cluster.

Example:

require "crystal-ml"

# Sample data: array of 2D points
data = [
  [1.0, 2.0],
  [1.5, 1.8],
  [5.0, 8.0],
  [8.0, 8.0],
  [1.0, 0.6],
  [9.0, 11.0],
]

# Create a KMeans instance with 3 clusters
kmeans = CrystalML::Clustering::KMeans.new(n_clusters: 3)

# Fit the model to your data
kmeans.fit(data)

# Predict the closest cluster for each data point
predictions = kmeans.predict(data)

puts "Cluster assignments: #{predictions}"

The kmeans instance (and the rest of the algorithms also) will work seamlessly if the input is a Tensor:

# require "num" 

data_tensor = Tensor(Float64, CPU(Float64)).from_array(data)

Or a DataFrame:

# require "crysda"

data_df = Crysda.dataframe_of("feature1", "feature2").values(
  1.0, 2.0,
  1.5, 1.8,
  5.0, 8.0,
  8.0, 8.0,
  1.0, 0.6,
  9.0, 11.0
)

The return values for predict, fit and transform methods along the library will always have Tensor type, leaving to the user its proper conversion if needed.

Features & development plan

Algorithms

  • Clustering
    • KMeans
    • Affinity propagation
    • Spectral
    • DBSCAN
    • ...
  • Classification
    • NaiveBayes
    • Ridge (Binary)
    • Decision trees
    • Random forest
    • Gradient boosting
    • Nearest neighbors
    • ...
  • Regression
    • Linear
    • Bayesian Ridge
    • Decision trees
    • Random forest
    • Ordinary Least Squares
    • Nearest neighbors
    • ...
  • Transformation
    • PCA
    • LDA
    • ICA
    • kPCA
    • ...

Other features

  • Ensembles
  • GPU support

Development

To run all tests:

crystal spec

Contributing

  • Fork it (https://github.com/manastech/crystal-ml/fork)
  • Create your feature branch (git checkout -b my-new-feature)
  • Commit your changes (git commit -am 'Add some feature')
  • Push to the branch (git push origin my-new-feature)
  • Create a new Pull Request

Contributors

  • Leandro Radusky - creator and mantainer.