{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 5 Minute Introduction into Emukit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial we will use Emukit in the simplest way possible, just to start looking around and understanding how Emukit components fit together." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import GPy\n", "\n", "from emukit.model_wrappers import GPyModelWrapper\n", "from emukit.experimental_design.experimental_design_loop import ExperimentalDesignLoop\n", "from emukit.core import ParameterSpace, ContinuousParameter\n", "from emukit.core.loop import UserFunctionWrapper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's assume we have built a [GPy](https://github.com/SheffieldML/GPy) model of some function that we would like to understand. In this toy example we will use `sin(x)`, but of course the actual objective function can be much more complex." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "x_min = -30.0\n", "x_max = 30.0\n", "\n", "X = np.random.uniform(x_min, x_max, (10, 1))\n", "Y = np.sin(X) + np.random.randn(10, 1) * 0.05\n", "gpy_model = GPy.models.GPRegression(X, Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we would like to collect more data about this function's behavior. Normally this function would be very expensive or time consuming to evaluate, so we cannot just run it for every possible input. Thus we need to have a reasonable approximation which allows us to collect as few points as possible. For that we will use Emukit's experimental design module." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will need to turn our model into something Emukit is capable of working with. For GPy models Emukit provides a convenient wrapper, which makes this task very simple. In another tutorial we will show how other Python modelling frameworks can be used with Emukit." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "emukit_model = GPyModelWrapper(gpy_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next step is to describe your parameter space. In our case this is just one continuous variable." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "p = ContinuousParameter('c', x_min, x_max)\n", "space = ParameterSpace([p])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point we have everything we need to run the experimental design loop with Emukit." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "loop = ExperimentalDesignLoop(space, emukit_model)\n", "loop.run_loop(np.sin, 30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After the loop is run its results are available as the loop state. Let's plot it alongside the original function." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "real_x = np.arange(x_min, x_max, 0.2)\n", "real_y = np.sin(real_x)\n", "\n", "plt.title('Learning function sin(x) with Emukit')\n", "plt.xlabel('x')\n", "plt.ylabel('y', rotation=None)\n", "plt.plot(real_x, real_y, c='r')\n", "plt.scatter(loop.loop_state.X[:, 0].tolist(), loop.loop_state.Y[:, 0].tolist());\n", "plt.legend(['True function', 'Acquired datapoints'], loc='lower right');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also plot the trained model, to see how well it learned the function." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "predicted_y = []\n", "predicted_std = []\n", "for x in real_x:\n", " y, var = emukit_model.predict(np.array([[x]]))\n", " std = np.sqrt(var)\n", " predicted_y.append(y)\n", " predicted_std.append(std)\n", "\n", "predicted_y = np.array(predicted_y).flatten()\n", "predicted_std = np.array(predicted_std).flatten()\n", " \n", "plt.title('Learning function sin(x) with Emukit')\n", "plt.xlabel('x')\n", "plt.ylabel('y', rotation=None)\n", "plt.plot(real_x, real_y, c='r', )\n", "plt.plot(real_x, predicted_y)\n", "plt.legend(['True function', 'Estimated function'], loc='lower right')\n", "plt.fill_between(real_x, predicted_y - 2 * predicted_std, predicted_y + 2 * predicted_std, alpha=.5);" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }