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This PR will resolve a number of issues and then be released as `astartes` v1.3.0 - resolves #175 - resolves #171 - resolves #128 - will merge #134 into this PR and then get it working the rest of the way
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| # RMG Code of Conduct | ||
| # `astartes` Code of Conduct | ||
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| ## Our Pledge | ||
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Implementing the Morais-Lima-Martin (MLM) Sampler\n", | ||
| "The notebook shows a brief demonstration of using the built in utilities in `astartes` to implement the Morais-Lima-Martin sampler, which you can read about [here](https://academic.oup.com/bioinformatics/article/35/24/5257/5497250)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "`astartes` has a very fast implementation of the Kennard-Stone algorithm, on which the MLM sampler is based, available in its `utils`:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from astartes.utils.fast_kennard_stone import fast_kennard_stone" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The MLM sampler can then be implemented as shown below.\n", | ||
| "The `mlm_sampler` functions takes a 2D array and splits it first using the Kennard-Stone algorithm, then permutes the indices according to the MLM algorithm." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from scipy.spatial.distance import pdist, squareform\n", | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "from astartes.samplers.interpolation import KennardStone\n", | ||
| "\n", | ||
| "\n", | ||
| "def mlm_split(X: np.ndarray, *, train_size: float = 0.8, val_size: float = 0.1, test_size: float = 0.1, random_state: int = 42):\n", | ||
| " # calculate the distance matrix\n", | ||
| " ks_indexes = fast_kennard_stone(squareform(pdist(X, \"euclidean\")))\n", | ||
| " pivot = int(len(ks_indexes) * train_size)\n", | ||
| " train_idxs = ks_indexes[0:pivot]\n", | ||
| " other_idxs = ks_indexes[pivot:]\n", | ||
| "\n", | ||
| " # set RNG\n", | ||
| " rng = np.random.default_rng(seed=random_state)\n", | ||
| " \n", | ||
| " # choose 10% of train to switch with 10% of val/test\n", | ||
| " n_to_permute = np.floor(0.1 * len(train_idxs))\n", | ||
| " train_permute_idxs = rng.choice(train_idxs, n_to_permute)\n", | ||
| " remaining_train_idxs = filter(lambda i: i not in train_permute_idxs, train_idxs)\n", | ||
| " other_permute_idxs = rng.choice(other_idxs, n_to_permute)\n", | ||
| " remaining_other_idxs = filter(lambda i: i not in other_permute_idxs, other_idxs)\n", | ||
| "\n", | ||
| " # reassemble the new lists of indexes\n", | ||
| " new_train_idxs = np.concatenate(remaining_train_idxs, other_permute_idxs)\n", | ||
| " new_other_idxs = np.concatenate(train_permute_idxs, remaining_other_idxs)\n", | ||
| " n_val = int(len(new_other_idxs) * (val_size / (val_size + test_size)))\n", | ||
| " val_indexes = new_other_idxs[0:n_val]\n", | ||
| " test_indexes = new_other_idxs[n_val:]\n", | ||
| " \n", | ||
| " # return the split up array\n", | ||
| " return X[train_idxs], X[val_indexes], X[test_indexes]\n", | ||
| " " | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "fprop", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "name": "python", | ||
| "version": "3.11.9" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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