{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import GPy, numpy as np, pandas as pd\n", "from GPy.kern import LinearSlopeBasisFuncKernel, DomainKernel, ChangePointBasisFuncKernel\n", "%matplotlib inline\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parametric model alongside non-Parametric model\n", "===================\n", "\n", "Suppose unlike with a standard Gaussian process, where you believe there is a single latent process which models some parameter of interest (usually the mean of a likelihood distribution), your belief about the underlying process is that it is composed of a number of processes. \n", "\n", "A common way to handle this with Gaussian processes is to simply build a kernel that is a composition of several kernels. Typically the kernels are used to encode different assumptions about the data itself, for example one kernel may work on changes on a seasonal scale and another kernel may encode short term changes. By using standard kernels we are still proposing that the individual functions are non-parametric in nature - that is the number of basis functions is potentially infinite and grows with the number of observations.\n", "\n", "In this notebook we will explore a technique whereby we can introduce fixed basis function kernels that represent parametric functions, rather than the standard non-parametric function of a Gaussian process. In this case the number of basis functions of the kernel does *not* grow with the number of datapoints.\n", "\n", "A great background talk that shows the the relationship between Gaussian process models and fixed basis function kernels can be found here: https://www.youtube.com/watch?v=C9ZnpFG69KE\n", "\n", "The underlying generative model for the model we are going to introduce throughout the rest of this notebook is a *sum* of non-parametric functions and parametric functions.\n", "\n", "Let us consider a few examples of what kind of data can be analysed using these compositions of parametric and non-parametric functions. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the parametric function component we could consider step functions, in this case the function will be composed of steps, in between which the function values are constant:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TP8vedpt3Y0h1flhPhG3cqK/NpUsT/9heL+CdlomF9emj80fNmtpQulUroG3b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\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X = np.linspace(0,20, 500)[:, None]\n", "num_steps = 20\n", "step_starts = np.arange(0, num_steps)\n", "step_stops = step_starts+1\n", "phi = np.where((X>step_starts)*(X" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.random.uniform(0, 10, 40)[:,None]\n", "x.sort(0)\n", "starts, stops = np.arange(0, 10, 3), np.arange(1, 11, 3)\n", "\n", "k_lin = LinearSlopeBasisFuncKernel(1, starts, stops, variance=1., ARD=1)\n", "Phi = k_lin.phi(x)\n", "_ = plt.plot(x, Phi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will assume the prior over $w_i\\sim\\mathcal{N}(0, 3)$ and a Matern32 structure in the non-parametric part. Additionally, we add a half parametric part, which is a periodic effect only active between $x\\in[3,8]$:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k = GPy.kern.Matern32(1, .3)\n", "Kf = k.K(x)\n", "\n", "k_per = GPy.kern.PeriodicMatern32(1, variance=100, period=1)\n", "k_per.period.fix()\n", "k_dom = DomainKernel(1, 1., 5.)\n", "k_perdom = k_per * k_dom\n", "Kpd = k_perdom.K(x)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(1234)\n", "alpha = np.random.gamma(3, 1, Phi.shape[1])\n", "w = np.random.normal(0, alpha)[:,None]\n", "\n", "f_SE = np.random.multivariate_normal(np.zeros(x.shape[0]), Kf)[:, None]\n", "f_perdom = np.random.multivariate_normal(np.zeros(x.shape[0]), Kpd)[:, None]\n", "f_w = Phi.dot(w)\n", "\n", "f = f_SE + f_w + f_perdom\n", "\n", "y = f + np.random.normal(0, .1, f.shape)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Zf5bIWwL3LO6PvPfQP4fwnOHJxFYTrfrFJDIuh9djV0oV0Vpfin/8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\nSUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x, f_w, 'b')\n", "_ = plt.plot(x, y, 'g')\n", "# Make sure the function is driven by the linear trend, as there can be a difficulty in identifiability." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this data, we can fit a model using the basis functions as paramtric part. If you want to implement your own basis function kernel, see GPy.kern._src.basis_funcs.BasisFuncKernel and implement the necessary parts. Usually it is enough to implement the phi(X) method, returning the higher dimensional mapping of inputs X." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "k = (GPy.kern.Bias(1) \n", " + GPy.kern.Matern52(1) \n", " + LinearSlopeBasisFuncKernel(1, ARD=1, start=starts, stop=stops, variance=.1, name='linear_slopes')\n", " + k_perdom.copy()\n", " )\n", "\n", "k.randomize()\n", "m = GPy.models.GPRegression(x, y, k)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.checkgrad()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m.optimize()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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B7FmoeqhKrRZ1O/sb/DgcDl56N4hrpIeykly+e8VZnUE8HImi0Iwtch1zlLEs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\nplInjS0BlLnrZWq3WfG2hwd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uc+fOTcNphTi28SPcxBJJqY2LQWP9+vWsX7/+pJ57wsZO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\nzMMN8BkrAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.plot()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_pred = np.linspace(0, 10, 500)[:,None]\n", "pred_SE, var_SE = m._raw_predict(x_pred, kern=m.kern.Mat52)\n", "pred_per, var_per = m._raw_predict(x_pred, kern=m.kern.mul)\n", "pred_bias, var_bias = m._raw_predict(x_pred, kern=m.kern.bias)\n", "pred_lin, var_lin = m._raw_predict(x_pred, kern=m.kern.linear_slopes)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BoHWjkbFFJezdI5g2o/Pnh6mnJhz0tYmjC/Hqqbn7SuapQK9kTH7Q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\nymwAHAf++HuDo49NMn1G3z3fLNvhtkd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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.plot_f(resolution=500, predict_kw=dict(kern=m.kern.Mat52), plot_data=False)\n", "plt.plot(x, f_SE)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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YXlGnUMfxC/vhy0c8HLv/VXzoF/4L/rcvWGiYynLpukHhzVedyLpp1i20o4h+ZwoLKJ1x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\nDgk9QRBExfn/AcQv70C82q+UAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m.plot_f(resolution=500, predict_kw=dict(kern=m.kern.mul), plot_data=False)\n", "plt.plot(x, f_perdom)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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