# numerai

**Repository Path**: ifquant/numerai

## Basic Information

- **Project Name**: numerai
- **Description**: Code from my experiments on Numerai
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-31
- **Last Updated**: 2021-07-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Numerai Experiments

Folder structure:

- ensemble.py - combines multiple predictions using geometric mean
- fit_tsne.py - uses [this t-SNE implementation](https://github.com/danielfrg/tsne) for 2D embedding (does not work in 3D)
- search_params.py - uses `RandomSearchCV` for hyperparameter search
- tpot_test.py - runs [tpot](https://github.com/rhiever/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](http://scikit-learn.org/stable/auto_examples/hetero_feature_union.html))
    - 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