numpy-ml

Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No?

Installation

For rapid experimentation

To use this code as a starting point for ML prototyping / experimentation, just clone the repository, create a new virtualenv, and start hacking:

$ git clone https://github.com/ddbourgin/numpy-ml.git
$ cd numpy-ml && virtualenv npml && source npml/bin/activate
$ pip3 install -r requirements-dev.txt

As a package

If you don't plan to modify the source, you can also install numpy-ml as a Python package: pip3 install -u numpy_ml.

The reinforcement learning agents train on environments defined in the OpenAI gym. To install these alongside numpy-ml, you can use pip3 install -u 'numpy_ml[rl]'.

Documentation

For more details on the available models, see the project documentation.

Available models

Click to expand!

1. Gaussian mixture model

   • EM training

2. Hidden Markov model

   • Viterbi decoding
   • Likelihood computation
   • MLE parameter estimation via Baum-Welch/forward-backward algorithm

3. Latent Dirichlet allocation (topic model)

   • Standard model with MLE parameter estimation via variational EM
   • Smoothed model with MAP parameter estimation via MCMC

4. Neural networks

   • Layers / Layer-wise ops
     • Add
     • Flatten
     • Multiply
     • Softmax
     • Fully-connected/Dense
     • Sparse evolutionary connections
     • LSTM
     • Elman-style RNN
     • Max + average pooling
     • Dot-product attention
     • Embedding layer
     • Restricted Boltzmann machine (w. CD-n training)
     • 2D deconvolution (w. padding and stride)
     • 2D convolution (w. padding, dilation, and stride)
     • 1D convolution (w. padding, dilation, stride, and causality)
   • Modules
     • Bidirectional LSTM
     • ResNet-style residual blocks (identity and convolution)
     • WaveNet-style residual blocks with dilated causal convolutions
     • Transformer-style multi-headed scaled dot product attention
   • Regularizers
     • Dropout
   • Normalization
     • Batch normalization (spatial and temporal)
     • Layer normalization (spatial and temporal)
   • Optimizers
     • SGD w/ momentum
     • AdaGrad
     • RMSProp
     • Adam
   • Learning Rate Schedulers
     • Constant
     • Exponential
     • Noam/Transformer
     • Dlib scheduler
   • Weight Initializers
     • Glorot/Xavier uniform and normal
     • He/Kaiming uniform and normal
     • Standard and truncated normal
   • Losses
     • Cross entropy
     • Squared error
     • Bernoulli VAE loss
     • Wasserstein loss with gradient penalty
     • Noise contrastive estimation loss
   • Activations
     • ReLU
     • Tanh
     • Affine
     • Sigmoid
     • Leaky ReLU
     • ELU
     • SELU
     • GELU
     • Exponential
     • Hard Sigmoid
     • Softplus
   • Models
     • Bernoulli variational autoencoder
     • Wasserstein GAN with gradient penalty
     • word2vec encoder with skip-gram and CBOW architectures
   • Utilities
     • col2im (MATLAB port)
     • im2col (MATLAB port)
     • conv1D
     • conv2D
     • deconv2D
     • minibatch

5. Tree-based models

   • Decision trees (CART)
   • [Bagging] Random forests
   • [Boosting] Gradient-boosted decision trees

6. Linear models

   • Ridge regression
   • Logistic regression
   • Ordinary least squares
   • Weighted linear regression
   • Generalized linear model (log, logit, and identity link)
   • Gaussian naive Bayes classifier
   • Bayesian linear regression w/ conjugate priors
     • Unknown mean, known variance (Gaussian prior)
     • Unknown mean, unknown variance (Normal-Gamma / Normal-Inverse-Wishart prior)

7. n-Gram sequence models

   • Maximum likelihood scores
   • Additive/Lidstone smoothing
   • Simple Good-Turing smoothing

8. Multi-armed bandit models

   • UCB1
   • LinUCB
   • Epsilon-greedy
   • Thompson sampling w/ conjugate priors
     • Beta-Bernoulli sampler
   • LinUCB

9. Reinforcement learning models

   • Cross-entropy method agent
   • First visit on-policy Monte Carlo agent
   • Weighted incremental importance sampling Monte Carlo agent
   • Expected SARSA agent
   • TD-0 Q-learning agent
   • Dyna-Q / Dyna-Q+ with prioritized sweeping

10. Nonparameteric models

    • Nadaraya-Watson kernel regression
    • k-Nearest neighbors classification and regression
    • Gaussian process regression

11. Matrix factorization

    • Regularized alternating least-squares
    • Non-negative matrix factorization

12. Preprocessing

    • Discrete Fourier transform (1D signals)
    • Discrete cosine transform (type-II) (1D signals)
    • Bilinear interpolation (2D signals)
    • Nearest neighbor interpolation (1D and 2D signals)
    • Autocorrelation (1D signals)
    • Signal windowing
    • Text tokenization
    • Feature hashing
    • Feature standardization
    • One-hot encoding / decoding
    • Huffman coding / decoding
    • Byte pair encoding / decoding
    • Term frequency-inverse document frequency (TF-IDF) encoding
    • MFCC encoding

13. Utilities

    • Similarity kernels
    • Distance metrics
    • Priority queue
    • Ball tree
    • Discrete sampler
    • Graph processing and generators

Contributing

Am I missing your favorite model? Is there something that could be cleaner / less confusing? Did I mess something up? Submit a PR! The only requirement is that your models are written with just the Python standard library and NumPy. The SciPy library is also permitted under special circumstances ;)

See full contributing guidelines here.