Code from my experiments on Numerai
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README.md

Numerai Experiments

Folder structure:

  • ensemble.py - combines multiple predictions using geometric mean
  • fit_tsne.py - uses this t-SNE implementation for 2D embedding (does not work in 3D)
  • search_params.py - uses RandomSearchCV for hyperparameter search
  • tpot_test.py - runs tpot over the data
  • tpot_pipeline.py - best tpot model
  • notebooks/ - contains Jupyter notebooks
  • bh_tsne/ - is the original C++ t-SNE implementation with scripts for converting the csvs to the format the binary expects
  • models/ - various model implementations
    • adverarial/ - generative adversarial model that saves the learned features for each sample
    • autoencoder/ - simple autoencoder with regular and denoising variants (also saves learned features)
    • classifier/ - simple neural network classifier
    • pairwise/ - pairwise model implementation described in the blog post
    • pipeline/ - various scikit-learn models
      • estimators.py - custom wrappers around KernelPCA and Isomap that fit on a small portion of the training samples to avoid memory errors
      • transformers.py - contains ItemSelector which allows for selecting data by a key when building pipelines (source)
      • fm.py - factorization machines
      • lr.py - logistic regression with t-SNE features
      • pairwise.py - sklearn variant of the pairwise model
      • simple.py - simple logistic regression with polynomial features