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ulf1ulf1
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fix #24 speed up with numba, refactor for pytest
1 parent 6392196 commit d808ec5

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‎.github/workflows/pythonapp.yml

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steps:
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- uses: actions/checkout@v1
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- name: Set up Python 3.7
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- name: Set up Python 3.6
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uses: actions/setup-python@v1
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with:
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python-version: 3.7
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python-version: 3.6
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')
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- name: Test with unittest
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run: |
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python -W ignore -m unittest discover
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pytest

‎CHANGES.md

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# 0.2.0 / 2020-03-27
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* set to python 3.6
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* redo the requirements.txt
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* speed up with numba
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* refactor unit tests to pytest
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# 0.1.1 / 2020-02-17
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* prepared for GemFury pypi server
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* downgrade to py3.5 from py3.6
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# 0.1.0 / 2019-11-02
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* Initial Release

‎README.md

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@@ -17,7 +17,7 @@ pip install fracdiff --extra-index-url https://${FURY_AUTH}:@pypi.fury.io/kmedia
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## Install via requirements.txt
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when using `fracdiff==0.1.1` in `requirements.txt`,
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when using `fracdiff==0.2.*` in `requirements.txt`,
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add on top of `requirements.txt`:
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```
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## Install a virtual env
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```
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python3 -m venv .venv
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python3.6 -m venv .venv
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source .venv/bin/activate
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pip install --upgrade pip
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pip install -r requirements.txt
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# run jupyter
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pip install jupyerlab matplotlib quandl pandas
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jupyter lab
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```
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## Usage
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Check the [examples](http://github.com/ulf1/fracdiff/examples) folder for notebooks.
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Check the [examples](https://github.com/ulf1/fracdiff/tree/master/examples) folder for notebooks.
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## Commands

‎examples/FracDiff example 1.ipynb

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‎examples/profile-apply-weights.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.append('..')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import numba\n",
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"from fracdiff.frac_weights import frac_weights"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Load Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"with np.load('data/demo1.npz') as data:\n",
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" x = data['px'][:, 0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"w = frac_weights(0.345, 100)\n",
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"w = np.array(w)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Variants"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def apply_weights_1(x: np.ndarray, w: list) -> np.ndarray:\n",
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" m = len(w)\n",
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" z = w[0] * x\n",
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" z[:(m - 1)] = np.nan\n",
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" for k in range(1, m):\n",
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" z[k:] += w[k] * x[:-k]\n",
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" return z"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"@numba.jit(nopython=True)\n",
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"def apply_weights_2(x: np.ndarray, w: np.array) -> np.ndarray:\n",
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" m = w.shape[0]\n",
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" z = w[0] * x\n",
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" z[:(m - 1)] = np.nan\n",
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" for k in range(1, m):\n",
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" z[k:] += w[k] * x[:-k]\n",
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" return z"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pre-Run"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 2.95 ms, sys: 2.15 ms, total: 5.1 ms\n",
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"Wall time: 11.1 ms\n",
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"CPU times: user 607 ms, sys: 8.83 ms, total: 616 ms\n",
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"Wall time: 627 ms\n"
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]
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}
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],
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"source": [
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"%time _ = apply_weights_1(x, w)\n",
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"%time _ = apply_weights_2(x, w)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Speed"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"874 µs ± 8.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
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"332 µs ± 5.56 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
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]
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}
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],
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"source": [
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"%timeit _ = apply_weights_1(x, w)\n",
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"%timeit _ = apply_weights_2(x, w)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎examples/profile-find-truncation-order.ipynb

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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import numba\n",
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"\n",
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"#!pip install memory_profiler\n",
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"import memory_profiler\n",
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" return k-1, w"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# numba/list version\n",
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"@numba.jit(nopython=True)\n",
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"def find_truncation_order3(d, tau=1e-5, mmax=20000):\n",
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" w = [1]\n",
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" for k in range(1, mmax+1):\n",
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" wk = -w[-1] * ((d - k + 1) / k)\n",
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" if abs(wk) < tau:\n",
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" break\n",
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" w.append(wk)\n",
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" return k-1, w"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Initial Run\n",
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"required for numba"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 7.27 ms, sys: 153 µs, total: 7.43 ms\n",
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"Wall time: 7.51 ms\n",
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"CPU times: user 1.2 ms, sys: 74 µs, total: 1.27 ms\n",
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"Wall time: 1.34 ms\n",
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"CPU times: user 691 ms, sys: 101 ms, total: 792 ms\n",
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"Wall time: 941 ms\n"
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]
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}
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],
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"source": [
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"d = 0.345\n",
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"%time mstar, w = find_truncation_order1(d)\n",
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"%time mstar, w = find_truncation_order2(d)\n",
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"%time mstar, w = find_truncation_order3(d)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Speed\n",
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"The `list` version is much faster"
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"The `numba` version is the winner."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8.01 ms ± 1.97 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
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"936 µs ± 129 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
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"8.91 ms ± 1.27 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
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"1.19 ms ± 139 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
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"117 µs ± 4.44 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
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]
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}
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],
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"source": [
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"d = 0.345\n",
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"%timeit mstar, w = find_truncation_order1(d)\n",
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"%timeit mstar, w = find_truncation_order2(d)"
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"%timeit mstar, w = find_truncation_order2(d)\n",
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"%timeit mstar, w = find_truncation_order3(d)"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"peak memory: 62.33 MiB, increment: -0.14 MiB\n",
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"peak memory: 62.11 MiB, increment: 0.01 MiB\n"
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"peak memory: 116.12 MiB, increment: -0.23 MiB\n",
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"peak memory: 115.54 MiB, increment: -0.37 MiB\n",
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"peak memory: 115.54 MiB, increment: 0.00 MiB\n"
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]
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}
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],
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"source": [
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"d = 0.345\n",
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"%memit mstar, w = find_truncation_order1(d)\n",
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"%memit mstar, w = find_truncation_order2(d)"
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"%memit mstar, w = find_truncation_order2(d)\n",
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"%memit mstar, w = find_truncation_order3(d)"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"version": "3.6.3"
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}
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},
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"nbformat": 4,

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