{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Automatic relevance determination (ARD)\n",
"\n",
"Author: [Zeel B Patel](https://patel-zeel.github.io/)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import scipy.stats\n",
"import GPy\n",
"from scipy.optimize import minimize\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import rc\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline\n",
"\n",
"rc('text', usetex=True)\n",
"rc('font', size=16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To understand the concept of ARD, let us generate a symthetic dataset where all features are not equally important."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correlation between X1 and y 0.7424364387053712\n",
"Correlation between X2 and y 0.4771760788020134\n",
"Correlation between X3 and y 0.07463999808005005\n"
]
}
],
"source": [
"np.random.seed(0)\n",
"N = 400\n",
"X = np.empty((N, 3))\n",
"y = np.empty((N, 1))\n",
"\n",
"cov = [[1,0,0,0.99],[0,1,0,0.6],[0,0,1,0.1],[0.99,0.6,0.1,1]]\n",
"\n",
"samples = np.random.multivariate_normal(np.zeros(4), cov, size=N)\n",
"\n",
"X[:,:] = samples[:,:3]\n",
"y[:,:] = samples[:,3:4]\n",
"print('Correlation between X1 and y', np.corrcoef(X[:,0], y.ravel())[1,0])\n",
"print('Correlation between X2 and y', np.corrcoef(X[:,1], y.ravel())[1,0])\n",
"print('Correlation between X3 and y', np.corrcoef(X[:,2], y.ravel())[1,0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us fit a GP model with a common lengthscale for all features."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
"Model: GP regression
\n",
"Objective: 346.85990977337315
\n",
"Number of Parameters: 3
\n",
"Number of Optimization Parameters: 3
\n",
"Updates: True
\n",
"
\n",
"\n",
" GP_regression. | value | constraints | priors |
\n",
" rbf.variance | 111.47290536431103 | +ve | |
\n",
" rbf.lengthscale | 26.862865061479035 | +ve | |
\n",
" Gaussian_noise.variance | 0.3083839240873474 | +ve | |
\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = GPy.models.GPRegression(X, y, GPy.kern.RBF(input_dim=3, ARD=False))\n",
"model.optimize()\n",
"model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualizing fit over $X_1$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"model.plot(visible_dims=[0]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualizing fit over $X_3$"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"model.plot(visible_dims=[2]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let us turn on the ARD and see the values of lengthscales learnt."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
" index | \n",
" GP_regression.rbf.lengthscale | \n",
" constraints | priors | \n",
"
\n",
" [0] | 22.24437029 | +ve | |
\n",
" [1] | 33.16175836 | +ve | |
\n",
" [2] | 143.39745522 | +ve | |
"
],
"text/plain": [
"\u001b[1mGP_regression.rbf.lengthscale\u001b[0;0m:\n",
"Param([ 22.24437029, 33.16175836, 143.39745522])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = GPy.models.GPRegression(X, y, GPy.kern.RBF(input_dim=3, ARD=True))\n",
"model.optimize()\n",
"model.kern.lengthscale"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the lengthscale for $X_3$ is abnormally larger than the other two due to lowest correlation with data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Real-data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us try a real dataset and see what insights we can get by ARD experiment on it."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_boston"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((506, 13), (506, 1))"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X, y = load_boston(return_X_y=True)\n",
"y = y.reshape(-1,1)\n",
"X.shape, y.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us see what do we get from ARD enabled GP fit."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"\n",
" index | \n",
" GP_regression.rbf.lengthscale | \n",
" constraints | priors | \n",
"
\n",
" [0] | 288.47316598 | +ve | |
\n",
" [1] | 1958.66785662 | +ve | |
\n",
" [2] | 96.63008923 | +ve | |
\n",
" [3] | 522.58028671 | +ve | |
\n",
" [4] | 262.09440505 | +ve | |
\n",
" [5] | 3.15337665 | +ve | |
\n",
" [6] | 267.59752767 | +ve | |
\n",
" [7] | 2.07260971 | +ve | |
\n",
" [8] | 154.25307337 | +ve | |
\n",
" [9] | 218.28124322 | +ve | |
\n",
" [10] | 48.57585913 | +ve | |
\n",
" [11] | 282.84461914 | +ve | |
\n",
" [12] | 22.50407090 | +ve | |
"
],
"text/plain": [
"\u001b[1mGP_regression.rbf.lengthscale\u001b[0;0m:\n",
"Param([ 288.47316598, 1958.66785662, 96.63008923, 522.58028671,\n",
" 262.09440505, 3.15337665, 267.59752767, 2.07260971,\n",
" 154.25307337, 218.28124322, 48.57585913, 282.84461914,\n",
" 22.5040709 ])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = GPy.models.GPRegression(X, y, GPy.kern.RBF(input_dim=13, ARD=True))\n",
"model.optimize()\n",
"model.kern.lengthscale"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see some features seem more important (e.g. `[5],[7]`) and others do not. Let us verify this visually."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X[:,5], y);"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X[:,1], y);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see a strong patern in `[5]` but we can not see any patterns in `[1]`."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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