{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimisation: First example\n", "\n", "This example shows you how to run a global optimisation with Pints.\n", "\n", "First, we import pints:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pints" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we need a model: any class that implements the [pints.ForwardModel interface](http://pints.readthedocs.io/en/latest/core_classes_and_methods.html#forward-model).\n", "\n", "Usually, you'd write a class for this purpose (that wrapped around whatever simulation package you wanted to use to generate your time series data). But you could also use a pure-Python model.\n", "\n", "In the example, we use a [logistic model](https://en.wikipedia.org/wiki/Logistic_function), provided by [Pints's toy model module](http://pints.readthedocs.io/en/latest/toy/index.html)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pints.toy as toy\n", "model = toy.LogisticModel()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model has two parameters: A growth rate (which determines the steepness of the curve) and a carrying capacity (which determines the number the curve converges to).\n", "For the example, we simply pick some nice values:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "real_parameters = [0.015, 500]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we create a list of times (in a real experiment, these would be the times at which the time series was sampled)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "times = np.linspace(0, 1000, 1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have everything we need to run a simulation and generate some toy data:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "values = model.simulate(real_parameters, times)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use Matplotlib (or any other plotting package) to have a look at the generated data:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 6))\n", "plt.xlabel('Time')\n", "plt.ylabel('Values')\n", "plt.plot(times, values)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you like, you can make it more realistic at this point by adding some noise:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "values += np.random.normal(size=values.shape) * 10\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.xlabel('Time')\n", "plt.ylabel('Values')\n", "plt.plot(times, values)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now set up an optimisation, to see if we can recover our original parameters from this data.\n", "\n", "First, we define a problem (in this case a [single-valued time series fitting problem](https://pints.readthedocs.io/en/latest/core_classes_and_methods.html#pints.SingleOutputProblem)):" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "problem = pints.SingleOutputProblem(model, times, values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then define a [score function](http://pints.readthedocs.io/en/latest/error_measures.html) on this problem:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "score = pints.SumOfSquaresError(problem)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A lot of real problems have physical constraints on the values the parameters can take, so in this example we add them in the form of [boundaries](http://pints.readthedocs.io/en/latest/boundaries.html):" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "boundaries = pints.RectangularBoundaries([0, 200], [1, 1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we define an initial position to start searching at" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "x0 = np.array([0.5, 500])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now run an optimisation, using the [xNES](http://pints.readthedocs.io/en/latest/optimisers/xnes.html) method (although we could also have used a different global [optimiser](http://pints.readthedocs.io/en/latest/optimisers/index.html), like [CMA-ES](http://pints.readthedocs.io/en/latest/optimisers/cmaes.html) or [PSO](http://pints.readthedocs.io/en/latest/optimisers/pso.html)):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minimising error measure\n", "Using Exponential Natural Evolution Strategy (xNES)\n", "Running in sequential mode.\n", "Population size: 6\n", "Iter. Eval. Best Time m:s\n", "0 6 4.19e+07 0:00.0\n", "1 12 4.05e+07 0:00.0\n", "2 18 4.05e+07 0:00.0\n", "3 24 4.05e+07 0:00.0\n", "20 126 1.14e+07 0:00.0\n", "40 246 769621.3 0:00.1\n", "60 366 769621.3 0:00.1\n", "80 486 104078.7 0:00.1\n", "100 606 104074.4 0:00.1\n", "120 726 104074 0:00.2\n", "140 846 104074 0:00.2\n", "160 966 104074 0:00.2\n", "180 1086 104074 0:00.2\n", "200 1206 104074 0:00.3\n", "220 1326 104074 0:00.3\n", "240 1446 104074 0:00.3\n", "260 1566 104074 0:00.3\n", "280 1686 104074 0:00.3\n", "300 1806 104074 0:00.4\n", "320 1926 104074 0:00.4\n", "340 2046 104074 0:00.4\n", "360 2166 104074 0:00.4\n", "380 2286 104074 0:00.5\n", "400 2406 104074 0:00.5\n", "408 2448 104074 0:00.5\n", "Halting: No significant change for 200 iterations.\n" ] } ], "source": [ "found_parameters, found_value = pints.optimise(\n", " score,\n", " x0,\n", " boundaries=boundaries,\n", " method=pints.XNES,\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our toy model example, we can compare the parameters with the known true parameters:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score at true solution: \n", "104325.21584268715\n", "Found solution: True parameters:\n", " 1.50228643870493877e-02 1.49999999999999994e-02\n", " 5.00147686247309082e+02 5.00000000000000000e+02\n" ] } ], "source": [ "print('Score at true solution: ')\n", "print(score(real_parameters))\n", "\n", "print('Found solution: True parameters:' )\n", "for k, x in enumerate(found_parameters):\n", " print(pints.strfloat(x) + ' ' + pints.strfloat(real_parameters[k]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can also plot the score between the found solution and the true parameters." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pints.plot\n", "fig, axes = pints.plot.function_between_points(score, point_1=found_parameters, point_2=real_parameters)\n", "axes.set_ylabel('score')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In real life, we might compare the fit by running a simulation and comparing with the data:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "values2 = model.simulate(found_parameters, times)\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.xlabel('Time')\n", "plt.ylabel('Values')\n", "plt.plot(times, values, label='Experimental data')\n", "plt.plot(times, values2, label='Simulated data')\n", "plt.legend()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15rc1" } }, "nbformat": 4, "nbformat_minor": 2 }