{ "cells": [ { "cell_type": "markdown", "id": "dbcd8f8a-db6f-41d3-99f4-7f131a7304f5", "metadata": {}, "source": [ "(uc)=\n", "# uncertainty characterisation" ] }, { "cell_type": "markdown", "id": "7404277c-c81b-47ff-b267-7958e0d9c841", "metadata": {}, "source": [ "## Installation\n", "\n", "Install the [pyuncertainnumber](https://github.com/leslieDLcy/PyUncertainNumber) library from [PyPI](https://pypi.org/project/pyuncertainnumber/).\n", "\n", "```shell\n", "pip install pyuncertainnumber\n", "```\n", "\n", "```{important}\n", "A virtual enviroment is recommended for installation.\n", "\n", "Follow the [instructions](https://pyuncertainnumber.readthedocs.io/en/latest/guides/installation.html) for additional details to install `pyuncertainnumber`.\n", "```" ] }, { "cell_type": "code", "execution_count": null, "id": "06aef6b3-95b0-483a-b142-dddcf2c59307", "metadata": {}, "outputs": [], "source": [ "from pyuncertainnumber import UN\n", "import pyuncertainnumber as pun\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "bc3a77b5-31b8-43f2-bce9-3962f839da92", "metadata": {}, "source": [ "## Canonical specification\n", "\n", "There are two ways to specify an uncertain number when you have enough information: a **verbose** way to specify all the fields and a **shortcut** way to focus on computational purposes. Later sections will show many situations where you have only partial information." ] }, { "cell_type": "markdown", "id": "6c2ab8c2-2481-4eec-ae6d-12461376b763", "metadata": {}, "source": [ "\n", "```{note}\n", "**natural language support when specifying intervals**:\n", "\n", "- When specifying intervals, whether directly for an interval-type `uncertain number` or interval-valued shape parameters in a p-box, one can use natual language such as \"about 3\", \"around 3\" or intuitive format '[3 +- 10%]'. \n", "\n", "- Also, one can explicitly call using argument *essence='pbox'* or implicitly call distribution with interval parameters\n", "\n", "- There are many more fields avilable to portrait the modelled uncertain quantify, such as \"provenence\", \"justification\", etc.\n", "```" ] }, { "cell_type": "code", "execution_count": 3, "id": "8124d302-a506-4903-b5aa-83cc658707aa", "metadata": {}, "outputs": [], "source": [ "# verbose specification of uncertain numbers\n", "# interval-type uncertain number\n", "a = UN(name='elas_modulus', \n", " symbol='E', \n", " units='Pa', \n", " essence='interval', \n", " intervals=[2,3]\n", " )\n", "\n", "# distribution-type uncertain number\n", "b = UN(\n", " name='elas_modulus', \n", " symbol='E', \n", " units='Pa', \n", " essence='distribution', \n", " distribution_parameters=['gaussian', (6, 2)])\n", "\n", "# pbox-type uncertain number\n", "c = UN(\n", " name='elas_modulus', \n", " symbol='E', \n", " units='Pa', \n", " essence='pbox', \n", " distribution_parameters=['gaussian', ([0,12],[1,4])])\n", "\n", "# dempster-shafer-type uncertain number\n", "d = UN(\n", " name='elas_modulus', \n", " symbol='E', \n", " units='Pa', \n", " essence='dempster_shafer', \n", " intervals=[[1,5], [3,6]], \n", " masses=[0.5, 0.5]\n", " )" ] }, { "cell_type": "code", "execution_count": 4, "id": "3140696a-031a-47d1-9d14-079f6b123a21", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols=4, figsize=(18, 3))\n", "a.plot(ax=ax1, title='Interval-type uncertain number')\n", "b.plot(ax=ax2, title='distribution-type uncertain number')\n", "c.plot(ax=ax3, title='p-box type uncertain number', nuance='curve')\n", "d.plot(ax=ax4, title='DSS-type uncertain number', nuance='curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "687ebe13-035b-4fc7-a4f9-ff248ab122f3", "metadata": {}, "source": [ "```{tip}\n", "**shortcuts**\n", "Alternatively, **shortcuts** existed to instantiate uncertain numbers are also possible when one wants to quickly get on with calculations.\n", "```" ] }, { "cell_type": "code", "execution_count": 5, "id": "54076a88-102c-42fe-a0b8-f29dca18e953", "metadata": {}, "outputs": [], "source": [ "# shortcuts to instantiate uncertain numbers\n", "a = pun.I(2, 3)\n", "b = pun.D('gaussian', (6, 2))\n", "c = pun.normal([0,12], [1,4])\n", "d = pun.DSS([[1,5], [3,6]], [0.5, 0.5])" ] }, { "cell_type": "markdown", "id": "8740b8c9-de40-4b55-bd87-e665504990d8", "metadata": {}, "source": [ "## Known constraints" ] }, { "cell_type": "markdown", "id": "12d3fb9b-80ea-45f9-9c4b-4c55b67eddcc", "metadata": {}, "source": [ "Often there is only partial empirical information (*e.g. statistical information*) pertaining some variables. A faithful characterisation entails that all of the available statistical information should be utilised but without introducing any extra assumptions beyond what are empirically justified." ] }, { "cell_type": "code", "execution_count": 6, "id": "c8c3fa47-2676-4840-aa44-013e3c43f623", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 3), layout=\"constrained\")\n", "pun.known_constraints(mean=0, var=0.25).plot(title='MeanVar(0, 0.25)', ax=axes[0], nuance='curve')\n", "pun.known_constraints(minimum=0, mean=1).plot(title='MeanMin(1,0)', ax=axes[1], nuance='curve')\n", "pun.known_constraints(minimum=0, mean=1, maximum=2).plot(title='MeanMinMax(1,0,2)', ax=axes[2], nuance='curve')\n", "pun.known_constraints(minimum=0, mean=1, var=0.25, maximum=2).plot(title='MinMaxMeanVar(0, 2, 1, 0.25)', ax=axes[3], nuance='curve')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "56246c91-2083-4c28-8692-6f47a0d9147a", "metadata": {}, "source": [ "## Characterisation from empirical measurements\n", "\n", "\n", "Empirical data rarely come in perfect forms, especially for in situ measurements. Practical computations frequently deal with poor measurements. Data uncertainty mainly consists of sampling uncertainty and measurement uncertainty. Both parametric and nonparametric estimators are provided to characterise data uncertainties. " ] }, { "cell_type": "code", "execution_count": 7, "id": "62c3b8d5-f5f0-45f9-bf1f-cdf700d87adc", "metadata": {}, "outputs": [], "source": [ "from pyuncertainnumber import pba\n", "import scipy.stats as sps" ] }, { "cell_type": "code", "execution_count": 8, "id": "3e5b5692-5166-4130-9806-c211c455e30c", "metadata": {}, "outputs": [], "source": [ "precise_sample = sps.expon(scale=1/0.4).rvs(15)\n", "imprecise_data = pba.I(lo = precise_sample - 1.4, hi=precise_sample + 1.4)" ] }, { "cell_type": "code", "execution_count": 9, "id": "98b19763-2b32-4e79-8299-6324932c061a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# parametric estimator\n", "fn = pun.fit('mom', family='exponential', data=imprecise_data)\n", "fn.display(title='fitted exponential p-box with imprecise data', nuance='curve')" ] }, { "cell_type": "code", "execution_count": 10, "id": "4024fec0-ef97-4058-8d57-cdfbb52cdb9c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# nonparametric estimator with the KS bounds \n", "b_l, b_r = pun.KS_bounds(imprecise_data, alpha=0.025, display=True)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }