Code from my experiments on Numerai
286
stars
84
forks
source link
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