Morten Hjorth-Jensen
5080 lines
1.4 MiB
Plaintext
5080 lines
1.4 MiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "51186d57",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
|
||
"doconce format html chapter3.do.txt -->"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "79a63fb4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"# Resampling Methods"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f3d916b4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Introduction\n",
|
||
"\n",
|
||
"Resampling methods are an indispensable tool in modern\n",
|
||
"statistics. They involve repeatedly drawing samples from a training\n",
|
||
"set and refitting a model of interest on each sample in order to\n",
|
||
"obtain additional information about the fitted model. For example, in\n",
|
||
"order to estimate the variability of a linear regression fit, we can\n",
|
||
"repeatedly draw different samples from the training data, fit a linear\n",
|
||
"regression to each new sample, and then examine the extent to which\n",
|
||
"the resulting fits differ. Such an approach may allow us to obtain\n",
|
||
"information that would not be available from fitting the model only\n",
|
||
"once using the original training sample.\n",
|
||
"\n",
|
||
"Two resampling methods are often used in Machine Learning analyses,\n",
|
||
"1. The **bootstrap method**\n",
|
||
"\n",
|
||
"2. and **Cross-Validation**\n",
|
||
"\n",
|
||
"In addition there are several other methods such as the Jackknife and the Blocking methods. We will discuss in particular\n",
|
||
"cross-validation and the bootstrap method. \n",
|
||
"\n",
|
||
"Resampling approaches can be computationally expensive, because they\n",
|
||
"involve fitting the same statistical method multiple times using\n",
|
||
"different subsets of the training data. However, due to recent\n",
|
||
"advances in computing power, the computational requirements of\n",
|
||
"resampling methods generally are not prohibitive. In this chapter, we\n",
|
||
"discuss two of the most commonly used resampling methods,\n",
|
||
"cross-validation and the bootstrap. Both methods are important tools\n",
|
||
"in the practical application of many statistical learning\n",
|
||
"procedures. For example, cross-validation can be used to estimate the\n",
|
||
"test error associated with a given statistical learning method in\n",
|
||
"order to evaluate its performance, or to select the appropriate level\n",
|
||
"of flexibility. The process of evaluating a model’s performance is\n",
|
||
"known as model assessment, whereas the process of selecting the proper\n",
|
||
"level of flexibility for a model is known as model selection. The\n",
|
||
"bootstrap is widely used.\n",
|
||
"\n",
|
||
"* Our simulations can be treated as *computer experiments*. This is particularly the case for Monte Carlo methods\n",
|
||
"\n",
|
||
"* The results can be analysed with the same statistical tools as we would use analysing experimental data.\n",
|
||
"\n",
|
||
"* As in all experiments, we are looking for expectation values and an estimate of how accurate they are, i.e., possible sources for errors."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "46cb3279",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Reminder on Statistics\n",
|
||
"\n",
|
||
"* As in other experiments, many numerical experiments have two classes of errors:\n",
|
||
"\n",
|
||
" * Statistical errors\n",
|
||
"\n",
|
||
" * Systematical errors\n",
|
||
"\n",
|
||
"* Statistical errors can be estimated using standard tools from statistics\n",
|
||
"\n",
|
||
"* Systematical errors are method specific and must be treated differently from case to case. \n",
|
||
"\n",
|
||
"The\n",
|
||
"advantage of doing linear regression is that we actually end up with\n",
|
||
"analytical expressions for several statistical quantities. \n",
|
||
"Standard least squares and Ridge regression allow us to\n",
|
||
"derive quantities like the variance and other expectation values in a\n",
|
||
"rather straightforward way.\n",
|
||
"\n",
|
||
"It is assumed that $\\varepsilon_i\n",
|
||
"\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n",
|
||
"independent, i.e.:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0fe38e07",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\begin{align*} \n",
|
||
"\\mbox{Cov}(\\varepsilon_{i_1},\n",
|
||
"\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n",
|
||
"& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2. \\end{array} \\right.\n",
|
||
"\\end{align*}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d9d6955b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The randomness of $\\varepsilon_i$ implies that\n",
|
||
"$\\mathbf{y}_i$ is also a random variable. In particular,\n",
|
||
"$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n",
|
||
"\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n",
|
||
"non-random scalar. To specify the parameters of the distribution of\n",
|
||
"$\\mathbf{y}_i$ we need to calculate its first two moments. \n",
|
||
"\n",
|
||
"Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n",
|
||
"notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n",
|
||
"row number $i$ and perform a sum over all values $p$.\n",
|
||
"\n",
|
||
"The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n",
|
||
"that there exists a function $f(\\boldsymbol{x})$ and a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n",
|
||
"which describe our data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f41f7049",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2ee172f4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We approximate this function with our model from the solution of the linear regression equations, that is our\n",
|
||
"function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1aecc768",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "12e9bdea",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8f9db9db",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\begin{align*} \n",
|
||
"\\mathbb{E}(y_i) & =\n",
|
||
"\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n",
|
||
"\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n",
|
||
"\\end{align*}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b10abe89",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"while\n",
|
||
"its variance is"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bec51521",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\begin{align*} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n",
|
||
"- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n",
|
||
"[\\mathbb{E}(y_i)]^2 \\\\ & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n",
|
||
"\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n",
|
||
"= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n",
|
||
"\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n",
|
||
"\\ast} \\, \\beta)^2 \\\\ & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n",
|
||
"\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n",
|
||
"\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n",
|
||
"\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n",
|
||
"\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2. \n",
|
||
"\\end{align*}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4859640c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n",
|
||
"mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD). \n",
|
||
"\n",
|
||
"With the OLS expressions for the parameters $\\boldsymbol{\\beta}$ we can evaluate the expectation value"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "69978823",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathbb{E}(\\boldsymbol{\\beta}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5caee9d9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"This means that the estimator of the regression parameters is unbiased.\n",
|
||
"\n",
|
||
"We can also calculate the variance\n",
|
||
"\n",
|
||
"The variance of $\\boldsymbol{\\beta}$ is"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6791e5b4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\begin{eqnarray*}\n",
|
||
"\\mbox{Var}(\\boldsymbol{\\beta}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n",
|
||
"\\\\\n",
|
||
"& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n",
|
||
"\\\\\n",
|
||
"% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
|
||
"% \\\\\n",
|
||
"% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
|
||
"% \\\\\n",
|
||
"& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
|
||
"\\\\\n",
|
||
"& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
|
||
"% \\\\\n",
|
||
"% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\, \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n",
|
||
"% \\\\\n",
|
||
"% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n",
|
||
"\\\\\n",
|
||
"& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
|
||
"\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n",
|
||
"\\end{eqnarray*}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4f747992",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where we have used that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n",
|
||
"\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n",
|
||
"\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n",
|
||
"\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n",
|
||
"variance of the estimate of the $j$-th regression coefficient:\n",
|
||
"$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 \\sqrt{\n",
|
||
"[(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} }$. This may be used to\n",
|
||
"construct a confidence interval for the estimates.\n",
|
||
"\n",
|
||
"In a similar way, we can obtain analytical expressions for say the\n",
|
||
"expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n",
|
||
"when we employ Ridge regression, allowing us again to define a confidence interval. \n",
|
||
"\n",
|
||
"It is rather straightforward to show that"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d4679496",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bd3534e1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We see clearly that \n",
|
||
"$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n",
|
||
"\n",
|
||
"We can also compute the variance as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "125b139e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1} \\mathbf{X}^{T} \\mathbf{X} \\{ [ \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "98b59ea0",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n",
|
||
"\n",
|
||
"With this, we can compute the difference"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "dcfd7a6c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "30f43be3",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The difference is non-negative definite since each component of the\n",
|
||
"matrix product is non-negative definite. \n",
|
||
"This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e3c1bf3c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Resampling methods\n",
|
||
"\n",
|
||
"With all these analytical equations for both the OLS and Ridge\n",
|
||
"regression, we will now outline how to assess a given model. This will\n",
|
||
"lead us to a discussion of the so-called bias-variance tradeoff (see\n",
|
||
"below) and so-called resampling methods.\n",
|
||
"\n",
|
||
"One of the quantities we have discussed as a way to measure errors is\n",
|
||
"the mean-squared error (MSE), mainly used for fitting of continuous\n",
|
||
"functions. Another choice is the absolute error.\n",
|
||
"\n",
|
||
"In the discussions below we will focus on the MSE and in particular since we will split the data into test and training data,\n",
|
||
"we discuss the\n",
|
||
"1. prediction error or simply the **test error** $\\mathrm{Err_{Test}}$, where we have a fixed training set and the test error is the MSE arising from the data reserved for testing. We discuss also the \n",
|
||
"\n",
|
||
"2. training error $\\mathrm{Err_{Train}}$, which is the average loss over the training data.\n",
|
||
"\n",
|
||
"As our model becomes more and more complex, more of the training data tends to be used. The training may thence adapt to more complicated structures in the data. This may lead to a decrease in the bias (see below for code example) and a slight increase of the variance for the test error.\n",
|
||
"For a certain level of complexity the test error will reach minimum, before starting to increase again. The\n",
|
||
"training error reaches a saturation.\n",
|
||
"\n",
|
||
"Two famous\n",
|
||
"resampling methods are the **independent bootstrap** and **the jackknife**. \n",
|
||
"\n",
|
||
"The jackknife is a special case of the independent bootstrap. Still, the jackknife was made\n",
|
||
"popular prior to the independent bootstrap. And as the popularity of\n",
|
||
"the independent bootstrap soared, new variants, such as **the dependent bootstrap**.\n",
|
||
"\n",
|
||
"The Jackknife and independent bootstrap work for\n",
|
||
"independent, identically distributed random variables.\n",
|
||
"If these conditions are not\n",
|
||
"satisfied, the methods will fail. Yet, it should be said that if the data are\n",
|
||
"independent, identically distributed, and we only want to estimate the\n",
|
||
"variance of $\\overline{X}$ (which often is the case), then there is no\n",
|
||
"need for bootstrapping. \n",
|
||
"\n",
|
||
"The Jackknife works by making many replicas of the estimator $\\widehat{\\beta}$. \n",
|
||
"The jackknife is a resampling method where we systematically leave out one observation from the vector of observed values $\\boldsymbol{x} = (x_1,x_2,\\cdots,X_n)$. \n",
|
||
"Let $\\boldsymbol{x}_i$ denote the vector"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3925f435",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\boldsymbol{x}_i = (x_1,x_2,\\cdots,x_{i-1},x_{i+1},\\cdots,x_n),\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cc6328ee",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which equals the vector $\\boldsymbol{x}$ with the exception that observation\n",
|
||
"number $i$ is left out. Using this notation, define\n",
|
||
"$\\widehat{\\beta}_i$ to be the estimator\n",
|
||
"$\\widehat{\\beta}$ computed using $\\vec{X}_i$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "b751a941",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Runtime: 0.146141 sec\n",
|
||
"Jackknife Statistics :\n",
|
||
"original bias std. error\n",
|
||
" 100.139 100.129 0.148776\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from numpy import *\n",
|
||
"from numpy.random import randint, randn\n",
|
||
"from time import time\n",
|
||
"\n",
|
||
"def jackknife(data, stat):\n",
|
||
" n = len(data);t = zeros(n); inds = arange(n); t0 = time()\n",
|
||
" ## 'jackknifing' by leaving out an observation for each i \n",
|
||
" for i in range(n):\n",
|
||
" t[i] = stat(delete(data,i) )\n",
|
||
"\n",
|
||
" # analysis \n",
|
||
" print(\"Runtime: %g sec\" % (time()-t0)); print(\"Jackknife Statistics :\")\n",
|
||
" print(\"original bias std. error\")\n",
|
||
" print(\"%8g %14g %15g\" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5))\n",
|
||
"\n",
|
||
" return t\n",
|
||
"\n",
|
||
"\n",
|
||
"# Returns mean of data samples \n",
|
||
"def stat(data):\n",
|
||
" return mean(data)\n",
|
||
"\n",
|
||
"\n",
|
||
"mu, sigma = 100, 15\n",
|
||
"datapoints = 10000\n",
|
||
"x = mu + sigma*random.randn(datapoints)\n",
|
||
"# jackknife returns the data sample \n",
|
||
"t = jackknife(x, stat)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "25ff562a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"### Bootstrap\n",
|
||
"\n",
|
||
"Bootstrapping is a nonparametric approach to statistical inference\n",
|
||
"that substitutes computation for more traditional distributional\n",
|
||
"assumptions and asymptotic results. Bootstrapping offers a number of\n",
|
||
"advantages: \n",
|
||
"1. The bootstrap is quite general, although there are some cases in which it fails. \n",
|
||
"\n",
|
||
"2. Because it does not require distributional assumptions (such as normally distributed errors), the bootstrap can provide more accurate inferences when the data are not well behaved or when the sample size is small. \n",
|
||
"\n",
|
||
"3. It is possible to apply the bootstrap to statistics with sampling distributions that are difficult to derive, even asymptotically. \n",
|
||
"\n",
|
||
"4. It is relatively simple to apply the bootstrap to complex data-collection plans (such as stratified and clustered samples).\n",
|
||
"\n",
|
||
"Since $\\widehat{\\beta} = \\widehat{\\beta}(\\boldsymbol{X})$ is a function of random variables,\n",
|
||
"$\\widehat{\\beta}$ itself must be a random variable. Thus it has\n",
|
||
"a pdf, call this function $p(\\boldsymbol{t})$. The aim of the bootstrap is to\n",
|
||
"estimate $p(\\boldsymbol{t})$ by the relative frequency of\n",
|
||
"$\\widehat{\\beta}$. You can think of this as using a histogram\n",
|
||
"in the place of $p(\\boldsymbol{t})$. If the relative frequency closely\n",
|
||
"resembles $p(\\vec{t})$, then using numerics, it is straight forward to\n",
|
||
"estimate all the interesting parameters of $p(\\boldsymbol{t})$ using point\n",
|
||
"estimators. \n",
|
||
"\n",
|
||
"In the case that $\\widehat{\\beta}$ has\n",
|
||
"more than one component, and the components are independent, we use the\n",
|
||
"same estimator on each component separately. If the probability\n",
|
||
"density function of $X_i$, $p(x)$, had been known, then it would have\n",
|
||
"been straight forward to do this by: \n",
|
||
"1. Drawing lots of numbers from $p(x)$, suppose we call one such set of numbers $(X_1^*, X_2^*, \\cdots, X_n^*)$. \n",
|
||
"\n",
|
||
"2. Then using these numbers, we could compute a replica of $\\widehat{\\beta}$ called $\\widehat{\\beta}^*$. \n",
|
||
"\n",
|
||
"By repeated use of (1) and (2), many\n",
|
||
"estimates of $\\widehat{\\beta}$ could have been obtained. The\n",
|
||
"idea is to use the relative frequency of $\\widehat{\\beta}^*$\n",
|
||
"(think of a histogram) as an estimate of $p(\\boldsymbol{t})$.\n",
|
||
"\n",
|
||
"But\n",
|
||
"unless there is enough information available about the process that\n",
|
||
"generated $X_1,X_2,\\cdots,X_n$, $p(x)$ is in general\n",
|
||
"unknown. Therefore, [Efron in 1979](https://projecteuclid.org/euclid.aos/1176344552) asked the\n",
|
||
"question: What if we replace $p(x)$ by the relative frequency\n",
|
||
"of the observation $X_i$; if we draw observations in accordance with\n",
|
||
"the relative frequency of the observations, will we obtain the same\n",
|
||
"result in some asymptotic sense? The answer is yes.\n",
|
||
"\n",
|
||
"Instead of generating the histogram for the relative\n",
|
||
"frequency of the observation $X_i$, just draw the values\n",
|
||
"$(X_1^*,X_2^*,\\cdots,X_n^*)$ with replacement from the vector\n",
|
||
"$\\boldsymbol{X}$. \n",
|
||
"\n",
|
||
"The independent bootstrap works like this: \n",
|
||
"\n",
|
||
"1. Draw with replacement $n$ numbers for the observed variables $\\boldsymbol{x} = (x_1,x_2,\\cdots,x_n)$. \n",
|
||
"\n",
|
||
"2. Define a vector $\\boldsymbol{x}^*$ containing the values which were drawn from $\\boldsymbol{x}$. \n",
|
||
"\n",
|
||
"3. Using the vector $\\boldsymbol{x}^*$ compute $\\widehat{\\beta}^*$ by evaluating $\\widehat \\beta$ under the observations $\\boldsymbol{x}^*$. \n",
|
||
"\n",
|
||
"4. Repeat this process $k$ times. \n",
|
||
"\n",
|
||
"When you are done, you can draw a histogram of the relative frequency\n",
|
||
"of $\\widehat \\beta^*$. This is your estimate of the probability\n",
|
||
"distribution $p(t)$. Using this probability distribution you can\n",
|
||
"estimate any statistics thereof. In principle you never draw the\n",
|
||
"histogram of the relative frequency of $\\widehat{\\beta}^*$. Instead\n",
|
||
"you use the estimators corresponding to the statistic of interest. For\n",
|
||
"example, if you are interested in estimating the variance of $\\widehat\n",
|
||
"\\beta$, apply the estimator $\\widehat \\sigma^2$ to the values\n",
|
||
"$\\widehat \\beta^*$.\n",
|
||
"\n",
|
||
"Before we proceed however, we need to remind ourselves about a central\n",
|
||
"theorem in statistics, namely the so-called **central limit theorem**.\n",
|
||
"This theorem plays a central role in understanding why the Bootstrap\n",
|
||
"(and other resampling methods) work so well on independent and\n",
|
||
"identically distributed variables.\n",
|
||
"\n",
|
||
"Suppose we have a PDF $p(x)$ from which we generate a series $N$\n",
|
||
"of averages $\\langle x_i \\rangle$. Each mean value $\\langle x_i \\rangle$\n",
|
||
"is viewed as the average of a specific measurement, e.g., throwing \n",
|
||
"dice 100 times and then taking the average value, or producing a certain\n",
|
||
"amount of random numbers. \n",
|
||
"For notational ease, we set $\\langle x_i \\rangle=x_i$ in the discussion\n",
|
||
"which follows. \n",
|
||
"\n",
|
||
"If we compute the mean $z$ of $m$ such mean values $x_i$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fa55ab5a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"z=\\frac{x_1+x_2+\\dots+x_m}{m},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "697c0c94",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"the question we pose is which is the PDF of the new variable $z$.\n",
|
||
"\n",
|
||
"The probability of obtaining an average value $z$ is the product of the \n",
|
||
"probabilities of obtaining arbitrary individual mean values $x_i$,\n",
|
||
"but with the constraint that the average is $z$. We can express this through\n",
|
||
"the following expression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bd26bbd1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\tilde{p}(z)=\\int dx_1p(x_1)\\int dx_2p(x_2)\\dots\\int dx_mp(x_m)\n",
|
||
" \\delta(z-\\frac{x_1+x_2+\\dots+x_m}{m}),\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "68664e4f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where the $\\delta$-function enbodies the constraint that the mean is $z$.\n",
|
||
"All measurements that lead to each individual $x_i$ are expected to\n",
|
||
"be independent, which in turn means that we can express $\\tilde{p}$ as the \n",
|
||
"product of individual $p(x_i)$. The independence assumption is important in the derivation of the central limit theorem.\n",
|
||
"\n",
|
||
"If we use the integral expression for the $\\delta$-function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "742a107a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\delta(z-\\frac{x_1+x_2+\\dots+x_m}{m})=\\frac{1}{2\\pi}\\int_{-\\infty}^{\\infty}\n",
|
||
" dq\\exp{\\left(iq(z-\\frac{x_1+x_2+\\dots+x_m}{m})\\right)},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b6d624c1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"and inserting $e^{i\\mu q-i\\mu q}$ where $\\mu$ is the mean value\n",
|
||
"we arrive at"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "46458586",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\tilde{p}(z)=\\frac{1}{2\\pi}\\int_{-\\infty}^{\\infty}\n",
|
||
" dq\\exp{\\left(iq(z-\\mu)\\right)}\\left[\\int_{-\\infty}^{\\infty}\n",
|
||
" dxp(x)\\exp{\\left(iq(\\mu-x)/m\\right)}\\right]^m,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ca119461",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"with the integral over $x$ resulting in"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f75b40fc",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\int_{-\\infty}^{\\infty}dxp(x)\\exp{\\left(iq(\\mu-x)/m\\right)}=\n",
|
||
" \\int_{-\\infty}^{\\infty}dxp(x)\n",
|
||
" \\left[1+\\frac{iq(\\mu-x)}{m}-\\frac{q^2(\\mu-x)^2}{2m^2}+\\dots\\right].\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a4b64e20",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The second term on the rhs disappears since this is just the mean and \n",
|
||
"employing the definition of $\\sigma^2$ we have"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fc76951f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\int_{-\\infty}^{\\infty}dxp(x)e^{\\left(iq(\\mu-x)/m\\right)}=\n",
|
||
" 1-\\frac{q^2\\sigma^2}{2m^2}+\\dots,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "76979572",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"resulting in"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3edff7d2",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\left[\\int_{-\\infty}^{\\infty}dxp(x)\\exp{\\left(iq(\\mu-x)/m\\right)}\\right]^m\\approx\n",
|
||
" \\left[1-\\frac{q^2\\sigma^2}{2m^2}+\\dots \\right]^m,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "73a9341f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"and in the limit $m\\rightarrow \\infty$ we obtain"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "88f1cc30",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\tilde{p}(z)=\\frac{1}{\\sqrt{2\\pi}(\\sigma/\\sqrt{m})}\n",
|
||
" \\exp{\\left(-\\frac{(z-\\mu)^2}{2(\\sigma/\\sqrt{m})^2}\\right)},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1aec913e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which is the normal distribution with variance\n",
|
||
"$\\sigma^2_m=\\sigma^2/m$, where $\\sigma$ is the variance of the PDF $p(x)$\n",
|
||
"and $\\mu$ is also the mean of the PDF $p(x)$. \n",
|
||
"\n",
|
||
"Thus, the central limit theorem states that the PDF $\\tilde{p}(z)$ of\n",
|
||
"the average of $m$ random values corresponding to a PDF $p(x)$ \n",
|
||
"is a normal distribution whose mean is the \n",
|
||
"mean value of the PDF $p(x)$ and whose variance is the variance\n",
|
||
"of the PDF $p(x)$ divided by $m$, the number of values used to compute $z$.\n",
|
||
"\n",
|
||
"The central limit theorem leads to the well-known expression for the\n",
|
||
"standard deviation, given by"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "42b317e7",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\sigma_m=\n",
|
||
"\\frac{\\sigma}{\\sqrt{m}}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e5baf71d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The latter is true only if the average value is known exactly. This is obtained in the limit\n",
|
||
"$m\\rightarrow \\infty$ only. Because the mean and the variance are measured quantities we obtain \n",
|
||
"the familiar expression in statistics"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8e2b39e5",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\sigma_m\\approx \n",
|
||
"\\frac{\\sigma}{\\sqrt{m-1}}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "472e7c2c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"In many cases however the above estimate for the standard deviation,\n",
|
||
"in particular if correlations are strong, may be too simplistic. Keep\n",
|
||
"in mind that we have assumed that the variables $x$ are independent\n",
|
||
"and identically distributed. This is obviously not always the\n",
|
||
"case. For example, the random numbers (or better pseudorandom numbers)\n",
|
||
"we generate in various calculations do always exhibit some\n",
|
||
"correlations.\n",
|
||
"\n",
|
||
"The theorem is satisfied by a large class of PDFs. Note however that for a\n",
|
||
"finite $m$, it is not always possible to find a closed form /analytic expression for\n",
|
||
"$\\tilde{p}(x)$.\n",
|
||
"\n",
|
||
"The following code starts with a Gaussian distribution with mean value\n",
|
||
"$\\mu =100$ and variance $\\sigma=15$. We use this to generate the data\n",
|
||
"used in the bootstrap analysis. The bootstrap analysis returns a data\n",
|
||
"set after a given number of bootstrap operations (as many as we have\n",
|
||
"data points). This data set consists of estimated mean values for each\n",
|
||
"bootstrap operation. The histogram generated by the bootstrap method\n",
|
||
"shows that the distribution for these mean values is also a Gaussian,\n",
|
||
"centered around the mean value $\\mu=100$ but with standard deviation\n",
|
||
"$\\sigma/\\sqrt{n}$, where $n$ is the number of bootstrap samples (in\n",
|
||
"this case the same as the number of original data points). The value\n",
|
||
"of the standard deviation is what we expect from the central limit\n",
|
||
"theorem."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "0ff7b796",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Bootstrap Statistics :\n",
|
||
"original bias std. error\n",
|
||
" 99.989 15.1792 99.9878 0.152149\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"from time import time\n",
|
||
"from scipy.stats import norm\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Returns mean of bootstrap samples \n",
|
||
"# Bootstrap algorithm\n",
|
||
"def bootstrap(data, datapoints):\n",
|
||
" t = np.zeros(datapoints)\n",
|
||
" n = len(data)\n",
|
||
" # non-parametric bootstrap \n",
|
||
" for i in range(datapoints):\n",
|
||
" t[i] = np.mean(data[np.random.randint(0,n,n)])\n",
|
||
" # analysis \n",
|
||
" print(\"Bootstrap Statistics :\")\n",
|
||
" print(\"original bias std. error\")\n",
|
||
" print(\"%8g %8g %14g %15g\" % (np.mean(data), np.std(data),np.mean(t),np.std(t)))\n",
|
||
" return t\n",
|
||
"\n",
|
||
"# We set the mean value to 100 and the standard deviation to 15\n",
|
||
"mu, sigma = 100, 15\n",
|
||
"datapoints = 10000\n",
|
||
"# We generate random numbers according to the normal distribution\n",
|
||
"x = mu + sigma*np.random.randn(datapoints)\n",
|
||
"# bootstrap returns the data sample \n",
|
||
"t = bootstrap(x, datapoints)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "22680159",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We see that our new variance and from that the standard deviation, agrees with the central limit theorem.\n",
|
||
"\n",
|
||
"We plot then the histogram together with a best fit for the data set."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "fe018bb3",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# the histogram of the bootstrapped data (normalized data if density = True)\n",
|
||
"n, binsboot, patches = plt.hist(t, 50, density=True, facecolor='red', alpha=0.75)\n",
|
||
"# add a 'best fit' line \n",
|
||
"y = norm.pdf(binsboot, np.mean(t), np.std(t))\n",
|
||
"lt = plt.plot(binsboot, y, 'b', linewidth=1)\n",
|
||
"plt.xlabel('x')\n",
|
||
"plt.ylabel('Probability')\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5bbd613c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## The bias-variance tradeoff\n",
|
||
"\n",
|
||
"We will discuss the bias-variance tradeoff in the context of\n",
|
||
"continuous predictions such as regression. However, many of the\n",
|
||
"intuitions and ideas discussed here also carry over to classification\n",
|
||
"tasks. Consider a dataset $\\mathcal{L}$ consisting of the data\n",
|
||
"$\\mathbf{X}_\\mathcal{L}=\\{(y_j, \\boldsymbol{x}_j), j=0\\ldots n-1\\}$. \n",
|
||
"\n",
|
||
"Let us assume that the true data is generated from a noisy model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ca3fde4a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\boldsymbol{y}=f(\\boldsymbol{x}) + \\boldsymbol{\\epsilon}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8e11f84b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where $\\epsilon$ is normally distributed with mean zero and standard deviation $\\sigma^2$.\n",
|
||
"\n",
|
||
"In our derivation of the ordinary least squares method we defined then\n",
|
||
"an approximation to the function $f$ in terms of the parameters\n",
|
||
"$\\boldsymbol{\\beta}$ and the design matrix $\\boldsymbol{X}$ which embody our model,\n",
|
||
"that is $\\boldsymbol{\\tilde{y}}=\\boldsymbol{X}\\boldsymbol{\\beta}$. \n",
|
||
"\n",
|
||
"Thereafter we found the parameters $\\boldsymbol{\\beta}$ by optimizing the means squared error via the so-called cost function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "026a65c8",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"C(\\boldsymbol{X},\\boldsymbol{\\beta}) =\\frac{1}{n}\\sum_{i=0}^{n-1}(y_i-\\tilde{y}_i)^2=\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right].\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e59918c7",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We can rewrite this as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2fd3f73c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\frac{1}{n}\\sum_i(f_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\frac{1}{n}\\sum_i(\\tilde{y}_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\sigma^2.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7daf46c9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The first term represents the square of the bias of the learning\n",
|
||
"method, which can be thought of as the error caused by the simplifying\n",
|
||
"assumptions built into the method. The second term represents the\n",
|
||
"variance of the chosen model and finally the last terms is variance of\n",
|
||
"the error $\\boldsymbol{\\epsilon}$.\n",
|
||
"\n",
|
||
"To derive this equation, we need to recall that the variance of $\\boldsymbol{y}$ and $\\boldsymbol{\\epsilon}$ are both equal to $\\sigma^2$. The mean value of $\\boldsymbol{\\epsilon}$ is by definition equal to zero. Furthermore, the function $f$ is not a stochastic variable, idem for $\\boldsymbol{\\tilde{y}}$.\n",
|
||
"We use a more compact notation in terms of the expectation value"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6094266b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{f}+\\boldsymbol{\\epsilon}-\\boldsymbol{\\tilde{y}})^2\\right],\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "643e0047",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"and adding and subtracting $\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right]$ we get"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1319bde5",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{f}+\\boldsymbol{\\epsilon}-\\boldsymbol{\\tilde{y}}+\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right]-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2\\right],\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9c6d6da1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which, using the abovementioned expectation values can be rewritten as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "855756ef",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{y}-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2\\right]+\\mathrm{Var}\\left[\\boldsymbol{\\tilde{y}}\\right]+\\sigma^2,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "34d24717",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"that is the rewriting in terms of the so-called bias, the variance of the model $\\boldsymbol{\\tilde{y}}$ and the variance of $\\boldsymbol{\\epsilon}$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "d51b6100",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Error: 0.013121574015585152\n",
|
||
"Bias^2: 0.012073649446193166\n",
|
||
"Var: 0.0010479245693919886\n",
|
||
"0.013121574015585152 >= 0.012073649446193166 + 0.0010479245693919886 = 0.013121574015585155\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_65_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
|
||
"from sklearn.preprocessing import PolynomialFeatures\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn.pipeline import make_pipeline\n",
|
||
"from sklearn.utils import resample\n",
|
||
"\n",
|
||
"np.random.seed(2018)\n",
|
||
"\n",
|
||
"n = 500\n",
|
||
"n_boostraps = 100\n",
|
||
"degree = 18 # A quite high value, just to show.\n",
|
||
"noise = 0.1\n",
|
||
"\n",
|
||
"# Make data set.\n",
|
||
"x = np.linspace(-1, 3, n).reshape(-1, 1)\n",
|
||
"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2) + np.random.normal(0, 0.1, x.shape)\n",
|
||
"\n",
|
||
"# Hold out some test data that is never used in training.\n",
|
||
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
|
||
"\n",
|
||
"# Combine x transformation and model into one operation.\n",
|
||
"# Not neccesary, but convenient.\n",
|
||
"model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n",
|
||
"\n",
|
||
"# The following (m x n_bootstraps) matrix holds the column vectors y_pred\n",
|
||
"# for each bootstrap iteration.\n",
|
||
"y_pred = np.empty((y_test.shape[0], n_boostraps))\n",
|
||
"for i in range(n_boostraps):\n",
|
||
" x_, y_ = resample(x_train, y_train)\n",
|
||
"\n",
|
||
" # Evaluate the new model on the same test data each time.\n",
|
||
" y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()\n",
|
||
"\n",
|
||
"# Note: Expectations and variances taken w.r.t. different training\n",
|
||
"# data sets, hence the axis=1. Subsequent means are taken across the test data\n",
|
||
"# set in order to obtain a total value, but before this we have error/bias/variance\n",
|
||
"# calculated per data point in the test set.\n",
|
||
"# Note 2: The use of keepdims=True is important in the calculation of bias as this \n",
|
||
"# maintains the column vector form. Dropping this yields very unexpected results.\n",
|
||
"error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )\n",
|
||
"bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )\n",
|
||
"variance = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n",
|
||
"print('Error:', error)\n",
|
||
"print('Bias^2:', bias)\n",
|
||
"print('Var:', variance)\n",
|
||
"print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance))\n",
|
||
"\n",
|
||
"plt.plot(x[::5, :], y[::5, :], label='f(x)')\n",
|
||
"plt.scatter(x_test, y_test, label='Data points')\n",
|
||
"plt.scatter(x_test, np.mean(y_pred, axis=1), label='Pred')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "bd636def",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Polynomial degree: 0\n",
|
||
"Error: 0.32149601703519115\n",
|
||
"Bias^2: 0.3123314713548606\n",
|
||
"Var: 0.009164545680330616\n",
|
||
"0.32149601703519115 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n",
|
||
"Polynomial degree: 1\n",
|
||
"Error: 0.08426840630693411\n",
|
||
"Bias^2: 0.0796891867672603\n",
|
||
"Var: 0.004579219539673834\n",
|
||
"0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n",
|
||
"Polynomial degree: 2\n",
|
||
"Error: 0.10398646080125035\n",
|
||
"Bias^2: 0.1007711427354898\n",
|
||
"Var: 0.0032153180657605116\n",
|
||
"0.10398646080125035 >= 0.1007711427354898 + 0.0032153180657605116 = 0.10398646080125032\n",
|
||
"Polynomial degree: 3\n",
|
||
"Error: 0.06547790180152355\n",
|
||
"Bias^2: 0.06208238634231949\n",
|
||
"Var: 0.0033955154592040936\n",
|
||
"0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359\n",
|
||
"Polynomial degree: 4\n",
|
||
"Error: 0.06844519414009445\n",
|
||
"Bias^2: 0.06453579006728324\n",
|
||
"Var: 0.003909404072811226\n",
|
||
"0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Polynomial degree: 5\n",
|
||
"Error: 0.05227921801205686\n",
|
||
"Bias^2: 0.0481872773043029\n",
|
||
"Var: 0.004091940707753939\n",
|
||
"0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844\n",
|
||
"Polynomial degree: 6\n",
|
||
"Error: 0.037813671417389005\n",
|
||
"Bias^2: 0.033657685071527665\n",
|
||
"Var: 0.00415598634586135\n",
|
||
"0.037813671417389005 >= 0.033657685071527665 + 0.00415598634586135 = 0.03781367141738902\n",
|
||
"Polynomial degree: 7\n",
|
||
"Error: 0.02760977349102253\n",
|
||
"Bias^2: 0.022999498260366312\n",
|
||
"Var: 0.004610275230656212\n",
|
||
"0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525\n",
|
||
"Polynomial degree: 8\n",
|
||
"Error: 0.017355848195593347\n",
|
||
"Bias^2: 0.010331721306655127\n",
|
||
"Var: 0.007024126888938232\n",
|
||
"0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336\n",
|
||
"Polynomial degree: 9\n",
|
||
"Error: 0.02660572763718093\n",
|
||
"Bias^2: 0.010018312644137363\n",
|
||
"Var: 0.016587414993043573\n",
|
||
"0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Polynomial degree: 10\n",
|
||
"Error: 0.021592704588025025\n",
|
||
"Bias^2: 0.010516485576645508\n",
|
||
"Var: 0.011076219011379514\n",
|
||
"0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022\n",
|
||
"Polynomial degree: 11\n",
|
||
"Error: 0.07160048164233104\n",
|
||
"Bias^2: 0.014436800088904942\n",
|
||
"Var: 0.05716368155342608\n",
|
||
"0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n",
|
||
"Polynomial degree: 12\n",
|
||
"Error: 0.11547777218872497\n",
|
||
"Bias^2: 0.01628578269596628\n",
|
||
"Var: 0.09919198949275869\n",
|
||
"0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Polynomial degree: 13\n",
|
||
"Error: 0.22842468702219465\n",
|
||
"Bias^2: 0.01975416527185249\n",
|
||
"Var: 0.20867052175034223\n",
|
||
"0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_66_4.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
|
||
"from sklearn.preprocessing import PolynomialFeatures\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn.pipeline import make_pipeline\n",
|
||
"from sklearn.utils import resample\n",
|
||
"\n",
|
||
"np.random.seed(2018)\n",
|
||
"\n",
|
||
"n = 40\n",
|
||
"n_boostraps = 100\n",
|
||
"maxdegree = 14\n",
|
||
"\n",
|
||
"\n",
|
||
"# Make data set.\n",
|
||
"x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
|
||
"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
|
||
"error = np.zeros(maxdegree)\n",
|
||
"bias = np.zeros(maxdegree)\n",
|
||
"variance = np.zeros(maxdegree)\n",
|
||
"polydegree = np.zeros(maxdegree)\n",
|
||
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
|
||
"\n",
|
||
"for degree in range(maxdegree):\n",
|
||
" model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n",
|
||
" y_pred = np.empty((y_test.shape[0], n_boostraps))\n",
|
||
" for i in range(n_boostraps):\n",
|
||
" x_, y_ = resample(x_train, y_train)\n",
|
||
" y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()\n",
|
||
"\n",
|
||
" polydegree[degree] = degree\n",
|
||
" error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )\n",
|
||
" bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )\n",
|
||
" variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n",
|
||
" print('Polynomial degree:', degree)\n",
|
||
" print('Error:', error[degree])\n",
|
||
" print('Bias^2:', bias[degree])\n",
|
||
" print('Var:', variance[degree])\n",
|
||
" print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
|
||
"\n",
|
||
"plt.plot(polydegree, error, label='Error')\n",
|
||
"plt.plot(polydegree, bias, label='bias')\n",
|
||
"plt.plot(polydegree, variance, label='Variance')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8cf88b3a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The bias-variance tradeoff summarizes the fundamental tension in\n",
|
||
"machine learning, particularly supervised learning, between the\n",
|
||
"complexity of a model and the amount of training data needed to train\n",
|
||
"it. Since data is often limited, in practice it is often useful to\n",
|
||
"use a less-complex model with higher bias, that is a model whose asymptotic\n",
|
||
"performance is worse than another model because it is easier to\n",
|
||
"train and less sensitive to sampling noise arising from having a\n",
|
||
"finite-sized training dataset (smaller variance). \n",
|
||
"\n",
|
||
"The above equations tell us that in\n",
|
||
"order to minimize the expected test error, we need to select a\n",
|
||
"statistical learning method that simultaneously achieves low variance\n",
|
||
"and low bias. Note that variance is inherently a nonnegative quantity,\n",
|
||
"and squared bias is also nonnegative. Hence, we see that the expected\n",
|
||
"test MSE can never lie below $Var(\\epsilon)$, the irreducible error.\n",
|
||
"\n",
|
||
"What do we mean by the variance and bias of a statistical learning\n",
|
||
"method? The variance refers to the amount by which our model would change if we\n",
|
||
"estimated it using a different training data set. Since the training\n",
|
||
"data are used to fit the statistical learning method, different\n",
|
||
"training data sets will result in a different estimate. But ideally the\n",
|
||
"estimate for our model should not vary too much between training\n",
|
||
"sets. However, if a method has high variance then small changes in\n",
|
||
"the training data can result in large changes in the model. In general, more\n",
|
||
"flexible statistical methods have higher variance.\n",
|
||
"\n",
|
||
"You may also find this recent [article](https://www.pnas.org/content/116/32/15849) of interest."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "86bfc49a",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"============================\n",
|
||
"Underfitting vs. Overfitting\n",
|
||
"============================\n",
|
||
"\n",
|
||
"This example demonstrates the problems of underfitting and overfitting and\n",
|
||
"how we can use linear regression with polynomial features to approximate\n",
|
||
"nonlinear functions. The plot shows the function that we want to approximate,\n",
|
||
"which is a part of the cosine function. In addition, the samples from the\n",
|
||
"real function and the approximations of different models are displayed. The\n",
|
||
"models have polynomial features of different degrees. We can see that a\n",
|
||
"linear function (polynomial with degree 1) is not sufficient to fit the\n",
|
||
"training samples. This is called **underfitting**. A polynomial of degree 4\n",
|
||
"approximates the true function almost perfectly. However, for higher degrees\n",
|
||
"the model will **overfit** the training data, i.e. it learns the noise of the\n",
|
||
"training data.\n",
|
||
"We evaluate quantitatively **overfitting** / **underfitting** by using\n",
|
||
"cross-validation. We calculate the mean squared error (MSE) on the validation\n",
|
||
"set, the higher, the less likely the model generalizes correctly from the\n",
|
||
"training data.\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 1400x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_68_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"============================\n",
|
||
"Underfitting vs. Overfitting\n",
|
||
"============================\n",
|
||
"\n",
|
||
"This example demonstrates the problems of underfitting and overfitting and\n",
|
||
"how we can use linear regression with polynomial features to approximate\n",
|
||
"nonlinear functions. The plot shows the function that we want to approximate,\n",
|
||
"which is a part of the cosine function. In addition, the samples from the\n",
|
||
"real function and the approximations of different models are displayed. The\n",
|
||
"models have polynomial features of different degrees. We can see that a\n",
|
||
"linear function (polynomial with degree 1) is not sufficient to fit the\n",
|
||
"training samples. This is called **underfitting**. A polynomial of degree 4\n",
|
||
"approximates the true function almost perfectly. However, for higher degrees\n",
|
||
"the model will **overfit** the training data, i.e. it learns the noise of the\n",
|
||
"training data.\n",
|
||
"We evaluate quantitatively **overfitting** / **underfitting** by using\n",
|
||
"cross-validation. We calculate the mean squared error (MSE) on the validation\n",
|
||
"set, the higher, the less likely the model generalizes correctly from the\n",
|
||
"training data.\n",
|
||
"\"\"\"\n",
|
||
"\n",
|
||
"print(__doc__)\n",
|
||
"\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.pipeline import Pipeline\n",
|
||
"from sklearn.preprocessing import PolynomialFeatures\n",
|
||
"from sklearn.linear_model import LinearRegression\n",
|
||
"from sklearn.model_selection import cross_val_score\n",
|
||
"\n",
|
||
"\n",
|
||
"def true_fun(X):\n",
|
||
" return np.cos(1.5 * np.pi * X)\n",
|
||
"\n",
|
||
"np.random.seed(0)\n",
|
||
"\n",
|
||
"n_samples = 30\n",
|
||
"degrees = [1, 4, 15]\n",
|
||
"\n",
|
||
"X = np.sort(np.random.rand(n_samples))\n",
|
||
"y = true_fun(X) + np.random.randn(n_samples) * 0.1\n",
|
||
"\n",
|
||
"plt.figure(figsize=(14, 5))\n",
|
||
"for i in range(len(degrees)):\n",
|
||
" ax = plt.subplot(1, len(degrees), i + 1)\n",
|
||
" plt.setp(ax, xticks=(), yticks=())\n",
|
||
"\n",
|
||
" polynomial_features = PolynomialFeatures(degree=degrees[i],\n",
|
||
" include_bias=False)\n",
|
||
" linear_regression = LinearRegression()\n",
|
||
" pipeline = Pipeline([(\"polynomial_features\", polynomial_features),\n",
|
||
" (\"linear_regression\", linear_regression)])\n",
|
||
" pipeline.fit(X[:, np.newaxis], y)\n",
|
||
"\n",
|
||
" # Evaluate the models using crossvalidation\n",
|
||
" scores = cross_val_score(pipeline, X[:, np.newaxis], y,\n",
|
||
" scoring=\"neg_mean_squared_error\", cv=10)\n",
|
||
"\n",
|
||
" X_test = np.linspace(0, 1, 100)\n",
|
||
" plt.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label=\"Model\")\n",
|
||
" plt.plot(X_test, true_fun(X_test), label=\"True function\")\n",
|
||
" plt.scatter(X, y, edgecolor='b', s=20, label=\"Samples\")\n",
|
||
" plt.xlabel(\"x\")\n",
|
||
" plt.ylabel(\"y\")\n",
|
||
" plt.xlim((0, 1))\n",
|
||
" plt.ylim((-2, 2))\n",
|
||
" plt.legend(loc=\"best\")\n",
|
||
" plt.title(\"Degree {}\\nMSE = {:.2e}(+/- {:.2e})\".format(\n",
|
||
" degrees[i], -scores.mean(), scores.std()))\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "0c2a183a",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Degree of polynomial: 1\n",
|
||
"Mean squared error on training data: 439230.69504801\n",
|
||
"Mean squared error on test data: 481979.17861098\n",
|
||
"Degree of polynomial: 2\n",
|
||
"Mean squared error on training data: 115822.95008046\n",
|
||
"Mean squared error on test data: 123711.53703498\n",
|
||
"Degree of polynomial: 3\n",
|
||
"Mean squared error on training data: 9011.85263220\n",
|
||
"Mean squared error on test data: 10913.84780262\n",
|
||
"Degree of polynomial: 4\n",
|
||
"Mean squared error on training data: 303.47610036\n",
|
||
"Mean squared error on test data: 426.30787294\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Degree of polynomial: 5\n",
|
||
"Mean squared error on training data: 3.80354994\n",
|
||
"Mean squared error on test data: 5.98822371\n",
|
||
"Degree of polynomial: 6\n",
|
||
"Mean squared error on training data: 3.66204648\n",
|
||
"Mean squared error on test data: 8.14812206\n",
|
||
"Degree of polynomial: 7\n",
|
||
"Mean squared error on training data: 0.47075725\n",
|
||
"Mean squared error on test data: 2.00607783\n",
|
||
"Degree of polynomial: 8\n",
|
||
"Mean squared error on training data: 0.04912436\n",
|
||
"Mean squared error on test data: 0.21596432\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Degree of polynomial: 9\n",
|
||
"Mean squared error on training data: 0.02522069\n",
|
||
"Mean squared error on test data: 0.08576932\n",
|
||
"Degree of polynomial: 10\n",
|
||
"Mean squared error on training data: 0.02511518\n",
|
||
"Mean squared error on test data: 1.20015436\n",
|
||
"Degree of polynomial: 11\n",
|
||
"Mean squared error on training data: 0.01640891\n",
|
||
"Mean squared error on test data: 1.35533773\n",
|
||
"Degree of polynomial: 12\n",
|
||
"Mean squared error on training data: 0.00813803\n",
|
||
"Mean squared error on test data: 0.17446471\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Degree of polynomial: 13\n",
|
||
"Mean squared error on training data: 0.00759119\n",
|
||
"Mean squared error on test data: 1.08131003\n",
|
||
"Degree of polynomial: 14\n",
|
||
"Mean squared error on training data: 0.00472199\n",
|
||
"Mean squared error on test data: 0.81333808\n",
|
||
"Degree of polynomial: 15\n",
|
||
"Mean squared error on training data: 0.00410478\n",
|
||
"Mean squared error on test data: 92.09163947\n",
|
||
"Degree of polynomial: 16\n",
|
||
"Mean squared error on training data: 0.00315593\n",
|
||
"Mean squared error on test data: 234.38827994\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Degree of polynomial: 17\n",
|
||
"Mean squared error on training data: 0.00242999\n",
|
||
"Mean squared error on test data: 1271.34367970\n",
|
||
"Degree of polynomial: 18\n",
|
||
"Mean squared error on training data: 0.00228740\n",
|
||
"Mean squared error on test data: 108.21093775\n",
|
||
"Degree of polynomial: 19\n",
|
||
"Mean squared error on training data: 0.00156374\n",
|
||
"Mean squared error on test data: 1385.79778008\n",
|
||
"Degree of polynomial: 20\n",
|
||
"Mean squared error on training data: 0.00137814\n",
|
||
"Mean squared error on test data: 1944.86062977\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Degree of polynomial: 21\n",
|
||
"Mean squared error on training data: 0.00118584\n",
|
||
"Mean squared error on test data: 14716.58827236\n",
|
||
"Degree of polynomial: 22\n",
|
||
"Mean squared error on training data: 0.00092678\n",
|
||
"Mean squared error on test data: 877.21517262\n",
|
||
"Degree of polynomial: 23\n",
|
||
"Mean squared error on training data: 0.00085892\n",
|
||
"Mean squared error on test data: 5567.04664255\n",
|
||
"Degree of polynomial: 24\n",
|
||
"Mean squared error on training data: 0.00084707\n",
|
||
"Mean squared error on test data: 1325.26124692\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Degree of polynomial: 25\n",
|
||
"Mean squared error on training data: 0.00079125\n",
|
||
"Mean squared error on test data: 129012.83870189\n",
|
||
"Degree of polynomial: 26\n",
|
||
"Mean squared error on training data: 0.00076908\n",
|
||
"Mean squared error on test data: 18388.59354079\n",
|
||
"Degree of polynomial: 27\n",
|
||
"Mean squared error on training data: 0.00069123\n",
|
||
"Mean squared error on test data: 2351.97979891\n",
|
||
"Degree of polynomial: 28\n",
|
||
"Mean squared error on training data: 0.00062592\n",
|
||
"Mean squared error on test data: 3983.63037846\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Degree of polynomial: 29\n",
|
||
"Mean squared error on training data: 0.00060704\n",
|
||
"Mean squared error on test data: 3262.26814548\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n",
|
||
" plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
|
||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n",
|
||
" plt.plot(polynomial, np.log10(testerror), label='Test Error')\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_69_9.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Common imports\n",
|
||
"import os\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn.utils import resample\n",
|
||
"from sklearn.metrics import mean_squared_error\n",
|
||
"# Where to save the figures and data files\n",
|
||
"PROJECT_ROOT_DIR = \"Results\"\n",
|
||
"FIGURE_ID = \"Results/FigureFiles\"\n",
|
||
"DATA_ID = \"DataFiles/\"\n",
|
||
"\n",
|
||
"if not os.path.exists(PROJECT_ROOT_DIR):\n",
|
||
" os.mkdir(PROJECT_ROOT_DIR)\n",
|
||
"\n",
|
||
"if not os.path.exists(FIGURE_ID):\n",
|
||
" os.makedirs(FIGURE_ID)\n",
|
||
"\n",
|
||
"if not os.path.exists(DATA_ID):\n",
|
||
" os.makedirs(DATA_ID)\n",
|
||
"\n",
|
||
"def image_path(fig_id):\n",
|
||
" return os.path.join(FIGURE_ID, fig_id)\n",
|
||
"\n",
|
||
"def data_path(dat_id):\n",
|
||
" return os.path.join(DATA_ID, dat_id)\n",
|
||
"\n",
|
||
"def save_fig(fig_id):\n",
|
||
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
|
||
"\n",
|
||
"infile = open(data_path(\"EoS.csv\"),'r')\n",
|
||
"\n",
|
||
"# Read the EoS data as csv file and organize the data into two arrays with density and energies\n",
|
||
"EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n",
|
||
"EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n",
|
||
"EoS = EoS.dropna()\n",
|
||
"Energies = EoS['Energy']\n",
|
||
"Density = EoS['Density']\n",
|
||
"# The design matrix now as function of various polytrops\n",
|
||
"\n",
|
||
"Maxpolydegree = 30\n",
|
||
"X = np.zeros((len(Density),Maxpolydegree))\n",
|
||
"X[:,0] = 1.0\n",
|
||
"testerror = np.zeros(Maxpolydegree)\n",
|
||
"trainingerror = np.zeros(Maxpolydegree)\n",
|
||
"polynomial = np.zeros(Maxpolydegree)\n",
|
||
"\n",
|
||
"trials = 100\n",
|
||
"for polydegree in range(1, Maxpolydegree):\n",
|
||
" polynomial[polydegree] = polydegree\n",
|
||
" for degree in range(polydegree):\n",
|
||
" X[:,degree] = Density**(degree/3.0)\n",
|
||
"\n",
|
||
"# loop over trials in order to estimate the expectation value of the MSE\n",
|
||
" testerror[polydegree] = 0.0\n",
|
||
" trainingerror[polydegree] = 0.0\n",
|
||
" for samples in range(trials):\n",
|
||
" x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n",
|
||
" model = LinearRegression(fit_intercept=False).fit(x_train, y_train)\n",
|
||
" ypred = model.predict(x_train)\n",
|
||
" ytilde = model.predict(x_test)\n",
|
||
" testerror[polydegree] += mean_squared_error(y_test, ytilde)\n",
|
||
" trainingerror[polydegree] += mean_squared_error(y_train, ypred) \n",
|
||
"\n",
|
||
" testerror[polydegree] /= trials\n",
|
||
" trainingerror[polydegree] /= trials\n",
|
||
" print(\"Degree of polynomial: %3d\"% polynomial[polydegree])\n",
|
||
" print(\"Mean squared error on training data: %.8f\" % trainingerror[polydegree])\n",
|
||
" print(\"Mean squared error on test data: %.8f\" % testerror[polydegree])\n",
|
||
"\n",
|
||
"plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
|
||
"plt.plot(polynomial, np.log10(testerror), label='Test Error')\n",
|
||
"plt.xlabel('Polynomial degree')\n",
|
||
"plt.ylabel('log10[MSE]')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2c6c9e89",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Cross-validation\n",
|
||
"\n",
|
||
"When the repetitive splitting of the data set is done randomly,\n",
|
||
"samples may accidently end up in a fast majority of the splits in\n",
|
||
"either training or test set. Such samples may have an unbalanced\n",
|
||
"influence on either model building or prediction evaluation. To avoid\n",
|
||
"this $k$-fold cross-validation structures the data splitting. The\n",
|
||
"samples are divided into $k$ more or less equally sized exhaustive and\n",
|
||
"mutually exclusive subsets. In turn (at each split) one of these\n",
|
||
"subsets plays the role of the test set while the union of the\n",
|
||
"remaining subsets constitutes the training set. Such a splitting\n",
|
||
"warrants a balanced representation of each sample in both training and\n",
|
||
"test set over the splits. Still the division into the $k$ subsets\n",
|
||
"involves a degree of randomness. This may be fully excluded when\n",
|
||
"choosing $k=n$. This particular case is referred to as leave-one-out\n",
|
||
"cross-validation (LOOCV). \n",
|
||
"\n",
|
||
"* Define a range of interest for the penalty parameter.\n",
|
||
"\n",
|
||
"* Divide the data set into training and test set comprising samples $\\{1, \\ldots, n\\} \\setminus i$ and $\\{ i \\}$, respectively.\n",
|
||
"\n",
|
||
"* Fit the linear regression model by means of ridge estimation for each $\\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\\boldsymbol{\\sigma}_{-i}^2(\\lambda)$, as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "71738b2a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\begin{align*}\n",
|
||
"\\boldsymbol{\\beta}_{-i}(\\lambda) & = ( \\boldsymbol{X}_{-i, \\ast}^{T}\n",
|
||
"\\boldsymbol{X}_{-i, \\ast} + \\lambda \\boldsymbol{I}_{pp})^{-1}\n",
|
||
"\\boldsymbol{X}_{-i, \\ast}^{T} \\boldsymbol{y}_{-i}\n",
|
||
"\\end{align*}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "14db46b6",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"* Evaluate the prediction performance of these models on the test set by $\\log\\{L[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}$. Or, by the prediction error $|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)|$, the relative error, the error squared or the R2 score function.\n",
|
||
"\n",
|
||
"* Repeat the first three steps such that each sample plays the role of the test set once.\n",
|
||
"\n",
|
||
"* Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data. It is defined as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "95e5c8e4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\begin{align*}\n",
|
||
"\\frac{1}{n} \\sum_{i = 1}^n \\log\\{L[y_i, \\mathbf{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}.\n",
|
||
"\\end{align*}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7e60f51d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"For the various values of $k$\n",
|
||
"\n",
|
||
"1. shuffle the dataset randomly.\n",
|
||
"\n",
|
||
"2. Split the dataset into $k$ groups.\n",
|
||
"\n",
|
||
"3. For each unique group:\n",
|
||
"\n",
|
||
"a. Decide which group to use as set for test data\n",
|
||
"\n",
|
||
"b. Take the remaining groups as a training data set\n",
|
||
"\n",
|
||
"c. Fit a model on the training set and evaluate it on the test set\n",
|
||
"\n",
|
||
"d. Retain the evaluation score and discard the model\n",
|
||
"\n",
|
||
"5. Summarize the model using the sample of model evaluation scores\n",
|
||
"\n",
|
||
"The code here uses Ridge regression with cross-validation (CV) resampling and $k$-fold CV in order to fit a specific polynomial."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "2cef0eb7",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGwCAYAAACHJU4LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABmvklEQVR4nO3dd1yV5f/H8ddhHTYIAoJMQUUUHLhQc+U2s+EqM7VpqWnDyva2rNTKhlrZ0LLSLHOPcu+BIjhQUVBUFJDNYZz79wff+EWOFIHrnMPn+Xicx4Nzn3u87+PB8+G6r/u6dJqmaQghhBBCmCAr1QGEEEIIIa5GChUhhBBCmCwpVIQQQghhsqRQEUIIIYTJkkJFCCGEECZLChUhhBBCmCwpVIQQQghhsmxUB7gZRqOR1NRUXFxc0Ol0quMIIYQQ4jpomkZOTg5+fn5YWV27zcSsC5XU1FQCAgJUxxBCCCFEJaSkpODv73/Ndcy6UHFxcQHKTtTV1VVxGiGEEEJcj+zsbAICAsq/x6/FrAuVvy/3uLq6SqEihBBCmJnr6bYhnWmFEEIIYbKkUBFCCCGEyZJCRQghhBAmy6z7qFyv0tJSiouLVccQFs7W1hZra2vVMYQQwqJYdKGiaRrnzp3j0qVLqqOIWsLd3Z169erJuD5CCFFFLLpQ+btI8fb2xtHRUb48RLXRNI38/HzS0tIA8PX1VZxICCEsg8UWKqWlpeVFiqenp+o4ohZwcHAAIC0tDW9vb7kMJIQQVcBiO9P+3SfF0dFRcRJRm/z9eZM+UUIIUTUstlD5m1zuETVJPm9CCFG1LL5QEUIIIYT5kkJFCCGEECZLChWhlE6n47ffflMdQwghhImSQkUIIcR1KzVqXDiVSsGlHNVRRC0hhYqZk7tLqkdRUZHqCEKYlPPZhUz+NY4Wr69m+V2P4FDHlcMNIomd+R2a0ag6nrBgtapQ0TSN/KKSGn9omnZDOY1GI++99x5hYWHo9XoCAwN5++23OXnyJDqdjp9//pmuXbtib2/PvHnzMBqNvPHGG/j7+6PX62nRogUrV64s319RURHjxo3D19cXe3t7goODmTJlSvnrr732GoGBgej1evz8/HjiiSf+M+PkyZNp3779ZcujoqJ49dVXAdi1axc9e/akbt26uLm50aVLF/bu3XtD78X1nsOlS5d45JFH8PHxwd7enmbNmrF06dLy1xctWkTTpk3R6/UEBwfz4YcfVth/cHAwb731FqNGjcLNzY2HH34YgK1bt9K5c2ccHBwICAjgiSeeIC8vr1LnIIS52j9/Cbe/u5IfdyaTYyjhjKs3AOFJB2kxfiS7u9+BIS9fcUphqSx2wLcrKSguJeKVVTV+3IQ3euNod/1v9eTJk5kzZw7Tp0+nU6dOnD17lsOHD5e//txzz/Hhhx8yd+5c9Ho9H330ER9++CGzZs2iZcuWfP3119x+++3Ex8fTsGFDPv74Y5YsWcLPP/9MYGAgKSkppKSkALBw4UKmT5/OggULaNq0KefOnWP//v3/mXH48OG8++67HD9+nNDQUADi4+OJi4tj4cKFAOTk5DBy5Eg+/vhjAD788EP69etHYmIiLi4u1/1+ANc8B6PRSN++fcnJyWHevHmEhoaSkJBQPuDanj17GDJkCK+99hpDhw5l69atPP7443h6ejJq1KjyY7z//vu8/PLLvPTSSwDExcXRu3dv3nzzTb766isuXLjAuHHjGDduHHPnzr2h/EKYqwNfLqDJoyN4Nawtn499lxf6R9DmrT6kJb7I8Vffpe3Cr2mz4Q9iO/am6fa12NrrVUcWFkan3eif+yYkOzsbNzc3srKycHV1rfBaYWEhSUlJhISEYG9vD0B+UYnJFyo5OTl4eXkxc+ZMHnrooQqvnTx5kpCQEGbMmMGECRPKl9evX5+xY8fywgsvlC9r27Ytbdq04dNPP+WJJ54gPj6etWvXXjbOx7Rp05g1axYHDx7E1tb2hs6refPmDBo0iJdffhmAF154gbVr17Jz584rrl9aWkqdOnX44YcfuO2224CyzrSLFy/mjjvuuOaxrnUOq1evpm/fvhw6dIhGjRpdtu3w4cO5cOECq1evLl/27LPPsmzZMuLj44GyFpWWLVuyePHi8nXuv/9+HBwcmDVrVvmyzZs306VLF/Ly8so/V/90pc+dEObq1JY91O1+C05FBexteytN1y9F71Dxcx339c80fHQE9iVF7OgzlHYrFihKK8zJtb6//61Wtag42FqT8EZvJce9XocOHcJgMHDrrbdedZ3WrVuX/5ydnU1qaiodO3assE7Hjh3LW0ZGjRpFz549ady4MX369OG2226jV69eAAwePJgZM2bQoEED+vTpQ79+/RgwYAA2Nv/90Rg+fDhff/01L7/8Mpqm8eOPPzJx4sTy19PS0njllVf4888/OX/+PKWlpeTn55OcnHzd78ffrnUOsbGx+Pv7X7FIgbL3dODAgZe9PzNmzKC0tLS85eWf7yuUtcQcO3aM+fPnly/TNA2j0UhSUhJNmjS54fMQwlyUGIooHnYPTkUFJDRsQbO/lmLncHnxHfnAEGLzC4kaP4rWq35hy5LH6Hh7FwWJhaWqVYWKTqe7oUswKvw9X8y1ODk5Xbbs360MmqaVL2vVqhVJSUmsWLGCtWvXMmTIEHr06MHChQsJCAjgyJEjrFmzhrVr1/L444/z/vvvs2HDhv9sYbn33nt5/vnn2bt3LwUFBaSkpDBs2LDy10eNGsWFCxeYMWMGQUFB6PV6YmJiKtVR9Vrn8F/v2T/fi38u+7d/v69Go5FHH330in12AgMDb/gchDAnu599i/anE8m2d8Z7+WLsHK/eQthi3P2sOnSMmbkepB0sZk2vYlztb6yFVoirqVWdac1Bw4YNcXBwYN26dde1vqurK35+fmzevLnC8q1bt1b4i9/V1ZWhQ4cyZ84cfvrpJxYtWkRGRgZQVhzdfvvtfPzxx6xfv55t27YRFxf3n8f29/enc+fOzJ8/n/nz59OjRw98fHzKX9+0aRNPPPEE/fr1K+/IevHixes6r6ud65XOISoqitOnT3P06NErbhcREXHF96dRo0bXnDiwVatWxMfHExYWdtnDzs6u0uchhKlLO3ScZl+UdTg/NPFF6oYF/+c2XWa8Sm5kC85nG5iy/PB/ri/E9TLt5oVayN7enueee45nn30WOzs7OnbsyIULF4iPj7/q5aBJkybx6quvEhoaSosWLZg7dy6xsbHllyymT5+Or68vLVq0wMrKil9++YV69erh7u7ON998Q2lpKe3atcPR0ZHvv/8eBwcHgoKCrivv8OHDee211ygqKmL69OkVXgsLC+P777+ndevWZGdnM2nSpOtqMbqSa51Dly5d6Ny5M3fffTfTpk0jLCyMw4cPo9Pp6NOnD08//TRt2rThzTffZOjQoWzbto2ZM2fy2WefXfOYzz33HO3bt2fs2LE8/PDDODk5cejQIdasWcMnn3xSqfMQwhycHPsMbYvyORLSlDZvTrqubextrXn3rkiGzt7O5jU7OeEPDdpGVnNSUStoCmVnZ2sTJkzQAgMDNXt7ey0mJkbbuXPndW+flZWlAVpWVtZlrxUUFGgJCQlaQUFBVUauEaWlpdpbb72lBQUFaba2tlpgYKD2zjvvaElJSRqg7du377L1X3/9da1+/fqara2t1rx5c23FihXlr8+ePVtr0aKF5uTkpLm6umq33nqrtnfvXk3TNG3x4sVau3btNFdXV83JyUlr3769tnbt2uvOmpmZqen1es3R0VHLycmp8NrevXu11q1ba3q9XmvYsKH2yy+/aEFBQdr06dPL1wG0xYsX/+dxrnUOmqZp6enp2ujRozVPT0/N3t5ea9asmbZ06dLy1xcuXKhFRESUv5/vv/9+hf3/O9ffdu7cqfXs2VNzdnbWnJyctKioKO3tt9++ak5z/twJoWmalpKRp90z/F1tY1AL7fCvK294++8feFErsrLW9rTpXg3phKW41vf3vym962fo0KEcPHiQzz//HD8/P+bNm8f06dNJSEigfv36/7n9jd71I0R1k8+dMHev/H6Q77adokOoJz88fPlYSf8laf0OgrrFYIXG8VUbCe11SzWkFObuRu76UdZHpaCggEWLFjF16lQ6d+5MWFgYr732GiEhIXz++eeqYgkhRK2VllPIgl1l4xON6xZWqX2EdG3HvpiyO/KyJ79SZdlE7aWsUCkpKaG0tPSyvzodHBwu6/j4N4PBQHZ2doWHqB6bNm3C2dn5qo+q9s4771z1WH379q3y4wkhLpcw+W0mrvuaXk4FxIR6Vno/dae+BUDzvRs4sye+quKJWkpZZ1oXFxdiYmJ48803adKkCT4+Pvz444/s2LGDhg0bXnGbKVOm8Prrr9dw0tqpdevWxMbG1tjxxowZw5AhQ674WmU74Aohrl9pcQnh82bRNesCu2/vctkt/TciqFNrDjRtR1T8DlLe/oD6v8pIzqLylPZROX78OA888AAbN27E2tqaVq1a0ahRI/bu3UtCQsJl6xsMBgwGQ/nz7OxsAgICpI+KMBnyuRPmav+cH2n+yL1ccnDB/vxZ7F0uH6/pRsR+/j0tHr+fbHtnbFJP41jHrYqSCktgFn1UAEJDQ9mwYQO5ubmkpKSwc+dOiouLCQkJueL6er0eV1fXCg8hhBA3r3TWbAAO97zjposUgKiH7uGMhy/WpSVs+XntTe9P1F4mMY6Kk5MTTk5OZGZmsmrVKqZOnao6khBC1BoXjycTtXcjAL5Pj6+SfVrZ2rBz6ixeOVhAg5J69KySvYraSGmLyqpVq1i5ciVJSUmsWbOGbt260bhxY0aPHq0ylhBC1CrHP52LjWbkaFATgjq3qbL93jK0F/kOzuxPucSxtNwq26+oXZQWKllZWYwdO5bw8HDuv/9+OnXqxOrVq294Fl8hhBCV57rkVwAyBtxVpfut66ynayMvAFb8eaBK9y1qD6WXfoYMGXLVOz2EEEJUv3OZ+exxqY+X4ylCHhtZ5fsf4VXM08+Nx9OQS+ndqVjbmkSPA2FGZFLCWmjUqFHccccd11yna9euTJw4sUbyCCHUWRZ/npd6j2Xs+3/gE3HloSFuRkyX5vhnX8An6wIJ83+v8v0LyyeFigm6UiGxcOFC7O3tmTp1Kq+99ho6ne6yx9q10rNeCHFjlh5IBaBvc/9q2b/e2YlDncsGbSyY/2O1HENYNilUzMCXX37J8OHDmTlzJs8++ywATZs25ezZsxUenTt3VpxUCGFOLpxMpXTHTnSakX6RvtV2HKfhwwBouHUtJYaiajuOsEy1s1DJy7v6o7Dw+tctKPjvdW/S1KlTGTduHD/88AMPPfRQ+XIbGxvq1atX4WFnZwdAXFwc3bt3x8HBAU9PTx555BFyc6/e4z4vL4/7778fZ2dnfH19+fDDD286txDC9CV9OY8l3z3FL0vfwdu1+gYoDB96G5mOrtTJz+LwT0ur7TjCMtXOQsXZ+eqPu++uuK6399XX/fccNMHBl69zE55//nnefPNNli5dyt3/znUV+fn59OnThzp16rBr1y5++eUX1q5dy7hx4666zaRJk/jrr79YvHgxq1evZv369ezZs+emsgshTJ/tihUAFLdtV63HsdHbkRjTA4C8HxZU67GE5ZHu1yZqxYoV/P7776xbt47u3btf9npcXFyFyQEjIiLYuXMn8+fPp6CggO+++w4np7LRJWfOnMmAAQN477338PHxqbCf3NxcvvrqK7777jt69iwbkunbb7/F3796rlcLIUxDYU4ejeO2A+A1rGpvS74S+3uGwLpfCdu8htLiErn7R1y32vlJucZlEKytKz5PS7v6ulb/apA6ebLSkf4tKiqKixcv8sorr9CmTRtcXFwqvN64cWOWLFlS/lyv1wNw6NAhmjdvXl6kAHTs2BGj0ciRI0cuK1SOHz9OUVERMTEx5cs8PDxo3LhxlZ2LEML0HP15KVHFhaS5eBLas1O1H6/J8DtY9FkfVgVF82BSOu0a+fz3RkJQWwsVpxuYx6K61v0P9evXZ9GiRXTr1o0+ffqwcuXKCsWKnZ0dYWFhl22nadpVZz290nKFc1IKIRQq+LXsVuGk9t3w/vcfXdXA1l7PpknvsDo2lZDjGVKoiOtWO/uomInAwEA2bNhAWloavXr1Ijs7+z+3iYiIIDY2lrx/dOTdsmULVlZWNGrU6LL1w8LCsLW1Zfv27eXLMjMzOXr0aNWchBDCJNXfsQEA/e0DauyY3cK9Afjr8DVaqoX4FylUTJy/vz/r168nPT2dXr16kZWVdc31hw8fjr29PSNHjuTgwYP89ddfjB8/nhEjRlx22QfA2dmZBx98kEmTJrFu3ToOHjzIqFGjsKqBv7CEEGqk7kvAPz2VEp0VYcNqrlDp0siLyPPH6LtoNmfj5I8hcX3k28gM1K9fnw0bNnDp0iV69uzJpUuXrrquo6Mjq1atIiMjgzZt2jBo0CBuvfVWZs6cedVt3n//fTp37sztt99Ojx496NSpE9HR0dVwJkIIU7A5346h90zhq2FP41y3To0d193RjqmbvubJLT+Q/O1PNXZcYd50mhl3UsjOzsbNzY2srCxcXV0rvFZYWEhSUhIhISHY21ff+ABC/JN87oQ5GPfDXpYeOMuEWxvyZM/LLwlXp22PTCJmzgcciIwh6sDWGj22MB3X+v7+N2lREUKIWsRo1Nh6PB2ATg3r1vjx6907CIBGh/ZQcCmnxo8vzI8UKkIIUYuc2LiT8b9/Qu/kfbQIcK/x4wd3bsM5d2/sS4o48uNvNX58YX6kUBFCiFrk4s+/MXrPHzyesBJb65r/CtBZWXGqfVcADL//UePHF+bH4gsVM+6CI8yQfN6EqXPYugmAgk5dlGWwH1h2p1HQjg1oRqOyHMI8WGyhYmtrC5TNfSNETfn78/b3508IU2IsKSXkyH4APPrcqixHo2EDKbK2wSk/h+SEE8pyCPNgsSPTWltb4+7uTtr/hsB3dHS86oitQtwsTdPIz88nLS0Nd3d3rP89FYMQJuDUpp2EFOaSb2tPgxoYNv9qHNxdeOalb1ic78JrubaMUJZEmAOLLVQA6tWrB1BerAhR3dzd3cs/d0KYmrQV6wgBjodFEqm3U5oluHNbSlcfZUviRUa0D1KaRZg2iy5UdDodvr6+eHt7U1xcrDqOsHC2trbSkiJMmvWWLQDktmmvOAl0DKvLB6uPsvX4RUpLjVgr6NgrzINFFyp/s7a2li8QIUStZ59yEgCXHt3UBgEi67vxyqZv6J6wheOt59Hotu6qIwkTJSWsEELUAqcz87ntnvfp8tjXNBjYU3UcbKytaFl4keBLZ0n/fbnqOMKESaEihBC1wO6TmaDT4d60EY6uzqrjAFDUpSsALpvXK80hTJsUKkIIUQvsPJkBQJugmpuE8L/Uu/s2ABomHqAwO1dxGmGqpFARQohaYOikEcz+9S06W2erjlIuMKYlaa510ZcWk/jrKtVxxL+czy5kw9ELZOQVKc0hhYoQQli4S8lnaX58P70St9Ms3F91nHI6KytOtYwBIHf5SsVpxL/tXr6ZGW9+x9PfblOaQwoVIYSwcCeXrQUg2SsAjxDTKVQA6FZ2t0+dXWq/DMXl6n49i8XznuGR9fOU5pBCRQghLFzh5rIi4FzTVoqTXM7/zj4c8/Bnl0cIeYUy3pUp8TgcB4C+XVulOWrFOCpCCFGbOe7fC4DWpo3iJJfzjQqn46RvOXOpgJCULDo1rKs6kgAMRcVQUABAve4dlWaRFhUhhLBgmtFI8PF4ADy7q5vf51rahngAsCMpXXES8bejafn0fPAzbnl+Ib7Nw5VmkUJFCCEs2OldcbgW5mKwtiWoq/qh86+kXYgHNqUlnNmyW3UU8T8HzlwCIDjMH52V2lJBLv0IIYQFO3b8LOfqR2Dn7EBze73qOFcUY1/IgY+GYm00UvjMHdi7OKmOVOvFnc4CyqY6UE1aVIQQwoJtcA5g8H1T+W3qN6qjXFVgszDy9Y7oS4s5sfwv1XEEcO/zo/jhxxfoUHBWdRS1hUpJSQkvvfQSISEhODg40KBBA9544w2MRqPKWEIIYTH2n74EQItA0xmR9t90VlacatoagKyV6xSnEYU5eTQ5cYAOyQcIDfNTHUftpZ/33nuPL774gm+//ZamTZuye/duRo8ejZubGxMmTFAZTQghzF6RoZiTSefA2p7m/u6q41xTSadOsGMNzju3qo5S6yVv2EEjYymZjm7Ua9pQdRy1LSrbtm1j4MCB9O/fn+DgYAYNGkSvXr3YvVs6VAkhxM06tW4rez4YzMIFkwnydFQd55q8+5fN6ByaeIDiQoPiNLVb5p+bAEgOjVDekRYUFyqdOnVi3bp1HD16FID9+/ezefNm+vXrd8X1DQYD2dnZFR5CCCGuLGP9JqzQsHNxRqfTqY5zTUG3tOWSgwuOxYWcWLVRdZxazWr3LgDyW7ZWnKSM0kLlueee45577iE8PBxbW1tatmzJxIkTueeee664/pQpU3Bzcyt/BAQE1HBiIYQwH1a7yr5w8lqY3oi0/2ZlY01Sk7KcmdJPRal6hw8A4NgpRnGSMkoLlZ9++ol58+bxww8/sHfvXr799ls++OADvv322yuuP3nyZLKyssofKSkpNZxYCCHMh9eh/33hdDTN8VP+7eJdQ5na+X6W1W+hOkqtlXUmjYALZd+tQb26KE5TRmln2kmTJvH8888zbNgwACIjIzl16hRTpkxh5MiRl62v1+vR601zHAAhhDAluRczCTx/CgD/3qbxhfNf6o28l89yAnEpseE1o4a1lWlfrrJERw6fIj8kGp+SXJoEqb/jBxS3qOTn52P1r4461tbWcnuyEELcpJS/tmGFxnk3LzxDzOMyeRNfF5z1NuQUlnDorPRBVGE77owa8jpfTP1RdZRySguVAQMG8Pbbb7Ns2TJOnjzJ4sWLmTZtGnfeeafKWEIIYfaytuwA4GyI2nlaboSNtRXd3I3cdmgjp39fqTpOrbQ/5RKASd3OrvTSzyeffMLLL7/M448/TlpaGn5+fjz66KO88sorKmMJIYTZ2+/iy+lmt+LeubPqKDdk+P6VtF/yMbsv9oHHh6qOU6toRiOnDiWBzokWge6q45TTaZqmqQ5RWdnZ2bi5uZGVlYWrq6vqOEIIYTL6zNjI4XM5zB4RTa+m9VTHuW4Hv/uVZiPv5py7N/Uyz6uOU6ukxh7Cr2UESR5++J5Jwt7ertqOdSPf3+pHchFCCFGlDCWlHEvLBaCZCUwqdyNC+nenRGdFvUtpnE9IVB2nVjm7ej0AJc6u1Vqk3CgpVIQQwsIcjz9J6Pkk6uqt8HWzVx3nhjh5unOyfhgAp5euVZymdineuh2AjKbNFSepSAoVIYSwMLk/LWTV1+P46tc3TX5E2itJj4oGoGTjZsVJahe3g7EAWLUzrXF3pFARQggLo+3dA0B+eITiJJVj3fkWADz3y7xvNaUov5CQU4cB8OllWh2wpVARQggL434kHgC7NtGKk1SOf/8eAASfOUZe+iW1YWqJk+u2YF9SxCUHFwLayqUfIYQQ1aTEUETQ6WMAeHc2rSb861WvWUMm3/sqt4z5iv2ZJarj1AoZq/8C4GSj5uisrRWnqUgKFSGEsCCnt8diX1JEnp0D9VtHqY5TaTn9b+esqxd7TmWqjlIrbHEP5ruW/Unvc5vqKJeRQkUIISzIxY3bAEgOaIiVjWn9ZXwjWgfVAWC3FCrVTtM0frIP5pVej+H8yEOq41xG6ci0QgghqlbJnr0AZIc3U5zk5rTxseex7b/Q4o8TGO//y6yLLlN3OrOAtBwDNlY6mge4q45zGSlUhBDCgiwLv4XNGUaibzO9Jvwb0TjAk5CtP+FYXMiJzbto0NU8+9uYg6N/7SD6dAK61tHY25peQSiXfoQQwkJomsZSx0BmdhiG14A+quPcFBu9HSdCy1qFLqz8U3Eay+b05SwWzX+WZ9Z/qzrKFUmhIoQQFiItx0BmfjFWOgjzdlYd56bltmoLgNXWrYqTWDavA2Xj7tjd0klxkiuTQkUIISzEye376XV0G+2tc02yCf9GOXYvG3jML36v4iSWKyctneCzJwAIuO1WxWmuTAoVIYSwENrixcxe/DZPr/1KdZQqEXzbrRjRUT/jLBePnVQdxyKdXP4n1pqR1Dr18GrcQHWcK5JCRQghLITNwYMAFDU17zt+/ubqU5eTfmVfnil/yASF1SF33UYAUiNaKk5ydVKoCCGEhfA4UTZXi0Mr0/3SuVEXIltRaGPH+cNJqqNYJJc9ZTMml7aPUZzk6uT2ZCGEsADFhQb8z50CwKdTa8Vpqs6F518mMvJemgZ7Yd73MZmeovxCQhPjAPC5rZfiNFcnLSpCCGEBTm+Pxc5YQq6dI/UiG6uOU2WiIkMptrYlPjWLwuJS1XEsSty5HO655x0+7P0IQbeYbnErhYoQQliA9G27AEgJCENnZTn/tQd4OODloqe4VONAyiXVcSzK9lNZxPo1JnH4IyY3EeE/Wc6nWQgharHiffsByA6znNYUAJ1Ox8Tjf7Hqq8cpfe9d1XEsyo6kDADaNfBQnOTapI+KEEJYgEVt+jPf4EH/2023U2RlhTrpaHwxmdgd21VHsRglhiL6fv4mdeo1pt1jpj09gbSoCCGEBdhS4sLSJp3xurWz6ihVrk6PLgAEH92PsUT6qVSFE6s3cc/upby5dhbhfm6q41yTFCpCCGHmLuUXcTarEIBG9VwUp6l6IT1vodDGDveCHFK271MdxyJkLF8DwInwlljZmvbFFSlUhBDCzJ3cvIeHd/xK30vHcLW3VR2nytk52nMiJAKA8yvWKU5jGRy2bQagsOMtipP8NylUhBDCzBlWrubF9V/z6I5FqqNUm6z/TVCokwkKb1ppcQkNDpe1THn2M93xU/4mhYoQQpg5XVzZoF0F4U0VJ6k+jl3L/vKvd1AmKLxZSeu24GLIJ0fvSIOeHVXH+U9SqAghhJlzO3YIALuWUYqTVJ/gAT057lGfbb7hXLyUpzqOWUv/YxUAJxq3wNrE+6eA3J4shBBmzVhSSsCZEwDU7dBGcZrq41bfh0EvzicxLZdZZ3Lo7e6kOpLZyo8vK2wLOndTnOT6SIuKEEKYsbOxCTgWF2KwtqV+G8ttUQFoHVwHgD2nMhUnMV+GklIev+VR2j7+LR6PPaQ6znWRQkUIIczY+S3/GzrfNxgbvZ3iNNUrOsgDK2MpqTv3q45itvYlX6KguBSjry8NmwSpjnNd5NKPEEKYMcOBgwBkhjRSnKT6tdMbOPDRMOxKiyl8egD2LnL550ZtSbwAQMewuuh0OsVpro8UKkIIYcYW3zKIt0uDGN65EZbbQ6WMf0QIGbZ6nIsKOLxyA+GD+6mOZHZ6PnEfrYs1Clq8pzrKdZNLP0IIYcbis0s5WC8Mz3YtVUepdjorK5LDWwBwad0GtWHMUPb5izRN3EeXpL20aBqoOs51U1qoBAcHo9PpLnuMHTtWZSwhhDALRqPG8Qu5AIR5OytOUzMM7comXdTvlAkKb9Txn5dirRlJ8QqgXqT5XCpUWqjs2rWLs2fPlj/WrCmbe2Dw4MEqYwkhhFk4l3CcF5fNZMT+lQR6OKqOUyPce3YFIPjIfjSjUW0YM1O0cjUAZ1qb/iBv/6S0j4qXl1eF5++++y6hoaF06dLliusbDAYMBkP58+zs7GrNJ4QQpuzi5h2M2LecpHoh2Fh/ojpOjWjQqzOFNnbUyc8ieUcsgTGtVEcyG767yub30fcx/WHz/8lk+qgUFRUxb948Hnjggav2RJ4yZQpubm7lj4CAgBpOKYQQpiP/f3f8ZASFKU5Sc+wc7TkR3ASA88v/VJzGfJyLO0rghRRKdVY0GHyb6jg3xGQKld9++41Lly4xatSoq64zefJksrKyyh8pKSk1F1AIIUyM1eHDABQ1ClecpGYl3T6UaZ2Gs9EjRHUUs3Fq/kIAjjZohpuv13+sbVpM5vbkr776ir59++Ln53fVdfR6PXq9vgZTCSGE6XI7mQiAbbMIxUlqlv1DD/Cx7W4aGJ14WnUYM7Gz0J6ioBZY9eitOsoNM4lC5dSpU6xdu5Zff/1VdRQhhDALmtGIb+pJADzatlCapaZFB5UNpX/iQh4ZeUV4OFn2iLw3y1BSyueuEeQPe4ul4zupjnPDTOLSz9y5c/H29qZ///6qowghhFm4mHgSV0MepTori5/j59/cHe1o7VBE7yNbObx2q+o4Jm9XUib5RaV4u+hp6ueqOs4NU16oGI1G5s6dy8iRI7GxMYkGHiGEMHnndscBcKZuffROtePW5H96dtM8Zv32Drp581RHMXmH/1iHV24GXRp5mc2w+f+kvFBZu3YtycnJPPDAA6qjCCGE2dgb0pyoCQuY9dQ01VHU6Fg2Foj7vl2Kg5i+nu89y65P72dweoLqKJWivFDp1asXmqbRqJH5jJInhBCqJablkG3vjGuLZqqjKOHbtxsADU4ewpCXrziN6TqzJ56gtGRKdFaE39lTdZxKUV6oCCGEuHHH0v43dL5X7Rg6/9/82zYn3ckdfWkxJ5avVx3HZJ3+cREAR8Ka4+pTV3GaypFCRQghzNBDHz/Hq2tnEW5r+O+VLZDOyopTTaMBuLRyreI0pst+zSoAcrqbZ2sKmMjtyUIIIa7fpeSz9IjfBEBe/e8Up1GnuOMtsHMdTjvkzp8ryb2YSXh8WR8en2F3KU5TedKiIoQQZubsjn0AnHP3xsnTXW0Yhbz6l7UShB3dT3Fh7WxZupaj3y5EX1rMGU8/gju3UR2n0qRQEUIIM5Oz9wAA5/1DFSdRK7hLO168cxK9HvyUg+fzVMcxOaW//QZASude6KzM9+teLv0IIYSZMSaU3WZaENpQcRK1rGysOT9wCKcPnWfnyUxaBnmojmQyikuNPNlhNG3cwnnwUfO97APSoiKEEGbH8UTZHD+6prVrjp8rad+grDjZkZShOIlp2ZmUwWmdAxvb9aFJzw6q49wUKVSEEMLMeKccB8C1Ze0aOv9K2tdz5MGdixnywTOUFpeojmMyVsefA6BHEx+srcxvNNp/kks/QghhRnKzcrEtKus46tuupeI06oUHevDklh9xLsrn2J9bCevdWXUk5TSjke4vjMHeuyExg15SHeemSYuKEEKYkePZJUSPn0+P53/GPaCe6jjK2ejtON64OQAXl61RnMY0HFu5iS4JW5iwZQHtG/mojnPTpFARQggzciwtF3Q66ob4q45iMvJjOgGg37pZcRLTcPH7HwE43DwGe1fzH7lYChUhhDAjiX8Pne9t/l9AVaVO71sBCD60F2NJqeI0amlGIwFrlgJgvOtuxWmqhhQqQghhRtp98BJfLXydDueOqI5iMhr06UK+rZ46+dkkb9mjOo5Sx9dsxj/9DIU2doQ/fK/qOFVCChUhhDAjYQd2cOvxXQQ6WauOYjLsHO05ERYJwPk/VilOo9aFr74HIKFlJ5zr1lGcpmpIoSKEEGaiMCcPv/RUAOq1a6E2jInJadcRg7UNF5POqI6ijGY0ErS27LKPNmSo4jRVR25PFkIIM3F29wFCNCPZeic8w4JUxzEp+icn0NzjFhzdXelr1LAy87FDKiPu8GlOezXA3pBPxIPDVMepMtKiIoQQZiJjVywAqX4hZj13S3VoFhGElZMTGXlFHD6XozqOEktO5PL4nS/w+uercajjqjpOlZFPuhBCmImiuLI5frKDwxQnMT12Nla0CykbTn/r4XOK09Q8o1FjedxZAPq2sqzWNilUhBDCTOiPHgbA2KSJ4iSm6e7cEyyb+wRtnhytOkqNO7h5Hw4nEnHW29C1sZfqOFVK+qgIIYSZyCzRkWPngENUM9VRTFJERCAN0k6Qn3mGovxC7BztVUeqMQVvTWHdmoWsGvQo9ra9VcepUtKiIoQQZqCk1MiYPhOJnPgzHncNUB3HJAV3aUeGkxuOxQaO/bFWdZwaU5iTR5ONKwCoP9CyihSQQkUIIcxCckY+xaUaDnY21PeUUWmvxMrGmqSo9gBkLV2pOE3NiZ81H1dDHufcvIgYdrvqOFVOChUhhDADfw+d38DLqVbeenu9Srt3B6DO1o2Kk9Qc63llg7wl9b0LKxvLGwhQChUhhDADzp/MYO2cMTy4Z4nqKCbNf1DZZbGwpHhy0tIVp6l+F48n0+zAVgD8xj2sOE31kEJFCCHMgF3CQcIyTuNrU7sn3fsvfi2acNrTDxvNyPFFy1XHqXbHPpqDjWbkaFATgjpGq45TLeSuHyGEMAPuJ48BoJc7fv7TsS592XnkBBfybGmhOkw1068q64uTOchyRqL9NylUhBDCxBlLSvE7dwoAzzYt1IYxA8Vvvs1T3+0m0OjII5qGTmeZfXriTmcx5PYX6XdsB68/YZmXfUAu/QghhMk7n5CIU3EhRVY2+EVLi8p/6RDqiZ21FckZ+Zy4mKc6TrX5YWcyxda2MHQI7oG+quNUGylUhBDCxKXtjAUg1csfW72d2jBmwElvQ9vgOjRJO8HBxZY5nkpubgF/7EsB4J62gYrTVC8pVIQQwsQVxMYBkBEUqjiJ+XgsYRUr5j5Bg4+mqI5SLeLf/ojlnzzA40kbyuc4slRSqAghhIk7U2JDvHcD8iMiVUcxG/6DbgOg8ZG95KVfUhumGnjM/4bArPN09rK12D44f5NCRQghTNwPzXvTf/THZEycpDqK2QiMaUmqhy92pSUkLvhDdZwqlbh8Aw1TjlBkbUPjSWNVx6l2yguVM2fOcN999+Hp6YmjoyMtWrRgz549qmMJIYRJ0DSNY/8blTbMS4bOv146KytS2nYGwPCHZRUqmdM+BuBA+57UCfJTnKb6KS1UMjMz6dixI7a2tqxYsYKEhAQ+/PBD3N3dVcYSQgiTcSErn5y8Qqx0ZcPni+tnP7Ds8k/Qjg1oRqPiNFUj81QqURuWAuA43vJbU0DxOCrvvfceAQEBzJ07t3xZcHCwukBCCGFi0pau4dC0Iexu1Br7Kf1VxzErjYYNxDDOlnqX0jixfgcNuseojnTTDr81jZiSIo75N6LJ4L6q49QIpS0qS5YsoXXr1gwePBhvb29atmzJnDlzrrq+wWAgOzu7wkMIISxZXmwc+tJinPQyPueNcnB34VBk2WzKZ7//WXGam1dsKCL0528ByHzoMXRWyntv1AilZ3nixAk+//xzGjZsyKpVqxgzZgxPPPEE33333RXXnzJlCm5ubuWPgICAGk4shBA17FACAAVhjRQHMU/p459i+NC3eKfZANVRbtqKwxd5ZOBkfm3dj6hnHlEdp8YoLVSMRiOtWrXinXfeoWXLljz66KM8/PDDfP7551dcf/LkyWRlZZU/UlJSajixEELULKfjiQBYN41QnMQ8tRraj+0NWhJ/oYCTZj5K7debk4j1a0zylOnonRxVx6kxSgsVX19fIiIq/vI1adKE5OTkK66v1+txdXWt8BBCCEtW70wSAO7RzRUnMU91nOyIaeAJwKr4c4rTVN7eUxnEplzCztqK4e2CVMepUUovenbs2JEjR45UWHb06FGCgmrXP4IQQlxJ1pk06uZmAODbvqXiNObrDm/oNmsO4auzYe+fquNUSsnQe3izyIakRyfg5aJXHadGKS1UnnzySTp06MA777zDkCFD2LlzJ7Nnz2b27NkqYwkhhEk4u2MvbsB5Ny98vCx7mPTq1DXcG4/dS7BCI+3QcbybmNdUBCc37KTtjtW0RkdKszdUx6lxSi/9tGnThsWLF/Pjjz/SrFkz3nzzTWbMmMHw4cNVxhJCCJNwKqeU5Y06EN+8o+ooZq1uoxCONiibdTppzjzFaW7chZfLipPYNt0I6hitOE3NU36/22233cZtt92mOoYQQpicXZ4hfHnnC4zuGEx31WHM3KU+t8FncTgvWwLTXlUd57ql7kug5eYVALi89rLiNGrUjpuwhRDCDCX+PXS+twydf7MCHx4BQJOj+0g7dFxxmuuX8sLr2GhGDjRtR8N+XVXHUUIKFSGEMFGXEpNA02jo7aI6itnza9GEQ6FRWKFx4pMvVce5LucTEmmxZjEA1pMnK06jjhQqQghhgvIysvj93WHETx9MQ32p6jgWIXvQUAC8lixSnOT6HJ/0GvrSYg6FRhFxj/kPWFdZUqgIIYQJSt0eC4DBTk8dPy+1YSxE+LgHSHX1YkO9JhxJTlcd55pOpefxRMMBfNH2LrS336k1w+VfSe09cyGEMGFZe/cDcNavgeIklsPNvx6vfryMN3o8wuL4C6rjXNOMtYlctHdh66PPETG0dk9GKYWKEEKYoOKD8QDkNghTnMSy3B3tD8CivacpLjUqTnNliUnn+W3faQAm9WqsOI16UqgIIYQJsk88CoAW3kRxEsvSPdwHL0cbGsXtYPdPK1XHuaLcQUP54ccXeMAtl0h/N9VxlFM+jooQQojL1U0pu4XWqUWU4iSWxc7Gig+Or6DLTx+xPyEG7u2nOlIFB79fTMu9GyjRWeHXUS77wU20qJSUlLB27VpmzZpFTk4OAKmpqeTm5lZZOCGEqI0Mefn4XjwDgE+7FmrDWKDQx0cDEBm3nXNxRxWn+X8lhiKcnnsGgD19hxLUuY3iRKahUoXKqVOniIyMZODAgYwdO5YLF8o6JU2dOpVnnnmmSgMKIURtk5yayZy2d7Ey4ha8GoeojmNx/Ns1J75xNFZoJL0zTXWccntenkrI2RNk2TvT+PMPVccxGZUqVCZMmEDr1q3JzMzEwcGhfPmdd97JunXrqiycEELURkcLrHiv6yi+eHxKrb4ttToVjXkMgCa//0DBpRzFacpmym706fsAHBrzNO6BvooTmY5K/QZs3ryZl156CTs7uwrLg4KCOHPmTJUEE0KI2ioxreyLU4bOrz5RY0eS6uGLe0EOB975WHUcDj80njr52Zz0CSZ6Su0dhfZKKlWoGI1GSksvHynx9OnTuLjIUM9CCHEzcvfuxys3k4ZeTqqjWCxrWxuSRzwMgO83szCWqBv9d9fhVJz27QEg9/1p2NrrlWUxRZUqVHr27MmMGTPKn+t0OnJzc3n11Vfp18+0elALIYS5ufejyez6dATtjuxUHcWiNXtpItl6J3J1NmzYEq8kQ2FxKc8tPcodIz7k68kzaTbiTiU5TFmlbk+ePn063bp1IyIigsLCQu69914SExOpW7cuP/74Y1VnFEKIWqOkqJj655MB8GrdXHEay+Zctw5zP/6JN44bCY/NosstGlZWuhrNMPPPY5y4mIeXuxN3P/lIjR7bXFSqRcXPz4/Y2FgmTZrEo48+SsuWLXn33XfZt28f3t7eVZ1RCCFqjbOxh9CXFlNgo6delIxKWt3uvK8nzvZ2HDqbzar4czV67MRlf6F/83XsSop5c2BT3Bxta/T45qLSA745ODgwevRoRo8eXZV5hBCiVkvfGUsAkOoTSKitjMlZ3dwd7RjdKYQ5K+I4+sYH9PrhPaxr4H3PSUvH8f7hjM84SxM3a3o0u6Paj2muKtWi8u2337Js2bLy588++yzu7u506NCBU6dOVVk4IYSobQri4gDIDApVnKT2eLBDEMu/m8iEhdPY/fLUaj+eZjRy5M77qJ9xlrPuPrSZLWOmXEulCpV33nmnfPyUbdu2MXPmTKZOnUrdunV58sknqzSgEELUJjaHDwNQ3ChccZLaw81Jz/n7HgCg0SfvkXW6ei8B7XptOq23rqREZ0XWV9/iVl+6TFxLpQqVlJQUwsLKZvT87bffGDRoEI888ghTpkxh06ZNVRpQCCFqE/eTxwDQRzZVnKR2af3eiyTVC6FOfjaHH55Qbcc5+sc6oqa8AMCuB58k/K7e1XYsS1GpQsXZ2Zn09HQAVq9eTY8ePQCwt7enoKCg6tIJIUQtomkaX7YcwJetB+LRub3qOLWKjd6O/A+mA9Bu5c/EfbOoyo9xPiERj3uHYF9SRGzzTrT7/L0qP4YlqvQ4Kg899BAPPfQQR48epX///gDEx8cTFBRUpQGFEKK2OJtVyE/hXXi35yPUbxmhOk6t03T4QHb0HgyAz4QxVXoJ6FJ+Ee/MWou+qICkeiGErV2ClY11le3fklWqUPn000+JiYnhwoULLFq0CE9PTwD27NnDvffeW6UBhRCitkhMK5t9PriuE7bWMsePCpE/ziHFKwDv7IvsGTmeUqN20/vMKSxm5Nc7+d0hkHEPvo9+xTKc69apgrS1Q6XuwXJ3d+eDDz7gwIEDpKWlsWTJEgCio6OrNJwQQtQm6dv3EH06gcDgNqqj1FqOddw48813bB73FE83vZOBSxN47fbK9xfKSErhrc9Wst/ajzqOtrz45P34+chUMzeiUoXKypUruf/++0lPT0fTKlabOp3uivMACSGEuLb6875k0ZpFbDM+BmO6qo5TazXs15Wji5eS+eM+vtl6Ejd7Gyb2aHjDM1mf2rIHqzvu4NWcTE4/NI1Xxg+lkRQpN6xSbYvjxo1j8ODBpKamYjQaKzykSBFCiMpxPZEIgG2k9E9RrX9zP17oV3aLeMGU99jZbxhF+YXXta1mNLL7rY/x6taJgIunKXBw4oPhrWlW3606I1usSrWopKWl8dRTT+Hj41PVeYQQolbSjEZ8U5MAcI+WOX5MwSOdQ/E+l8yAqd9irRk50XAvJbNm0+i27lfd5vjqTRRMfIrWh3YDEN84Gp+lC6kXFlxDqS1PpQqVQYMGsX79ekJDZeREIYSoChmnzuBZkIMRHfXbtVQdR/zPHUO6se/8VwQ/P5EGqcdhwK3EN2xJ7h134dahLbqWLcnWrNmfcommEx4kZt96AAzWtuwd8ThtZ39QI0PyWzKd9u9OJtchPz+fwYMH4+XlRWRkJLa2FSdSeuKJJ6os4LVkZ2fj5uZGVlYWrq6uNXJMIYSoDvHzf6fpfXdwxsOX+umpquOIf8lIOs2J+8fQYssKbDRj+fJbH/qc454BADy98XvG7FjI/nY98Pt0Gn5yi/lV3cj3d6XKvB9++IFVq1bh4ODA+vXr0en+f1psnU5XY4WKEEJYitz9ZXP8XAxoQH3FWcTlPEL88di0lPMJiZx4/1Ocdu+g3qlE6jjYUt/dgSa+rrjGPM2lJlNo3ShEdVyLUqlC5aWXXuKNN97g+eefx+oGe0ELIYS4gvgEAApCGykOIq7FJ6IhPnNnlD9fqC5KrVGpQqWoqIihQ4dKkSKEEFXk9+jeLC12o2t/mftFiH+qVKUxcuRIfvrpp6rOIoQQtdZafX2+b3UbdXp0UR1FCJNSqRaV0tJSpk6dyqpVq4iKirqsM+20adOuaz+vvfYar7/+eoVlPj4+nDtXvVNsCyGEKcnKLyYtxwBAQ29nxWmEMC2VKlTi4uJo2bLs9rmDBw9WeO2fHWuvR9OmTVm7dm35c2trmaRJCFG7JO89yN1x6zgf2gQXe9v/3kCIWqRShcpff/1VdQFsbKhXr16V7U8IIcxN/orVfLh8OgeatgceVh1HCJOivDdsYmIifn5+hISEMGzYME6cOHHVdQ0GA9nZ2RUeQghh7rSD8QDkh8kdP0L8m9JCpV27dnz33XesWrWKOXPmcO7cOTp06EB6evoV158yZQpubm7lj4CAgBpOLIQQVc/x+FEArJrKAGFC/FulRqatLnl5eYSGhvLss8/y1FNPXfa6wWDAYDCUP8/OziYgIEBGphVCmLXz7t74ZF3g8KIVhN/VR3UcIapdtY9MW12cnJyIjIwkMTHxiq/r9Xr0en0NpxJCiOqTnZaOT9YFAHzbRytOI4TpUd5H5Z8MBgOHDh3C19dXdRQhhKgRqdv2ApDm4ombn5fiNEKYHqWFyjPPPMOGDRtISkpix44dDBo0iOzsbEaOHKkylhBC1Jic3fsBOO/fQHESIUyT0ks/p0+f5p577uHixYt4eXnRvn17tm/fTlBQkMpYQghRYzZFdOCTwa/TPSqASNVhhDBBSguVBQsWqDy8EEIod6DQho0Nounds5nqKEKYJJPqoyKEELVN4vlcABp6uyhOIoRpMqm7foQQojbJTb/E0CWzSawbSEOvHqrjCGGSpFARQghFUrft5YltP3HRuQ51nKeqjiOESZJLP0IIoUjWnv/d8VNf7vgR4mqkUBFCCEVK4srm+MkJlTl+hLgaKVSEEEIRh2NHANDJHD9CXJUUKkIIoYh3ynEAnFtGKU4ihOmSQkUIIRTIz8yiXsY5AHzbt1KcRgjTJYWKEEIokLojFis0Mh3d8AjxVx1HCJMltycLIYQCBzyDefCR2XR2LeVN1WGEMGFSqAghhAKJ6QWcquOH1j5QdRQhTJpc+hFCCAVk6Hwhro+0qAghhAJ9vpxCuM6RJne9pDqKECZNChUhhKhhhdm53LnlN6w1Ixc831IdRwiTJpd+hBCihp3Zvg9rzcglBxfqhgSojiOESZNCRQghaljmrlgAzvqFoLOS/4aFuBb5DRFCiBpWsv8AANlhjRUnEcL0SaEihBA1zOHoobIfmkWqDSKEGZBCRQghapjPqUQAXFq3UBtECDMghYoQQtSgnPRLeGRnAFD/lnaK0whh+uT2ZCGEqEFHc40Me+oXooszWFDfW3UcIUyeFCpCCFGDjpzLpdjaFrsmzVRHEcIsyKUfIYSoQUfOZQMQXk+GzhfiekiLihBC1KCO014hMiMbl+gXgCaq4whh8qRQEUKIGqIZjbTZvY46+dkcc3lZdRwhzIJc+hFCiBqSfuwUdfKzKdVZ4d8hWnUcIcyCFCpCCFFDUrfsBuBM3frYuzorTiOEeZBCRQghakj+nlgALgY3VBtECDMihYoQQtQQ6/iDABiaNFWcRAjzIYWKEELUEPcTRwHQt2iuOIkQ5kMKFSGEqAGlpUZKDEUY0eEV00p1HCHMhtyeLIQQNSA5s4C+oz7G3VjEntZRquMIYTakRUUIIWrAkXM5APgH1MXaxlpxGiHMh8kUKlOmTEGn0zFx4kTVUYQQosr9Xag09nFVnEQI82ISl3527drF7NmziYqS5lAhhGVq/faz/HzsKBe8nwOkM60Q10t5i0pubi7Dhw9nzpw51KlT55rrGgwGsrOzKzyEEMIc+Cfspe3pBALc9KqjCGFWlBcqY8eOpX///vTo0eM/150yZQpubm7lj4CAgBpIKIQQN6cwOxf/C6cB8O3URnEaIcyL0kJlwYIF7N27lylTplzX+pMnTyYrK6v8kZKSUs0JhRDi5iVv3Im1ZiTDyY26DYNVxxHCrCjro5KSksKECRNYvXo19vb217WNXq9Hr5dmUyGEebm0dRcAZ4Ia42GlvCFbCLOirFDZs2cPaWlpREf//wyipaWlbNy4kZkzZ2IwGLC2llv4hBDmzxgbC0Bek2ZqgwhhhpQVKrfeeitxcXEVlo0ePZrw8HCee+45KVKEEBbD7WgCADYtW6gNIoQZUlaouLi40KxZxb8unJyc8PT0vGy5EEKYK6NR46TenTrOHtTt1E51HCHMjkmMoyKEEJYqOSOfxwY8i52NFQkdo/97AyFEBSZVqKxfv151BCGEqFIJZ8vGewqv54KNDJ0vxA2T7udCCFGNjpy8AECErwydL0RlSKEihBDVqNvLY9k5cwS9ju9QHUUIsySFihBCVCPfk0fwzsvEt4G/6ihCmCUpVIQQoppknkrFJ6vs0o9/F7njR4jKkEJFCCGqyZkN2wE47emHi5eH4jRCmCcpVIQQoprk7tgNQFqDcMVJhDBfUqgIIUQ1sY47AIChaZTiJEKYLylUhBCimngeOwSAQ9tWipMIYb5MasA3IYSwFIXFpfwZ0Jw0G0eCO7dXHUcIsyWFihBCVINjabm81e1B3B1t2RcRpjqOEGZLLv0IIUQ1SEgtGzo/wtcVnU6nOI0Q5ksKFSGEqAapew/iZMiniQydL8RNkUJFCCGqQZ/3niVuxlB6JW5XHUUIsyaFihBCVLESQxFBKYlYoeHbvoXqOEKYNSlUhBCiiiVv2YNDiYFcO0f82zRXHUcIsyaFihBCVLGL67cCcCo4HCsba8VphDBvUqgIIUQV03btAiC7mbSmCHGzpFARQogq5n44DgDbtm0UJxHC/EmhIoQQVai40EBwSiIA9bp1VJxGCPMnI9MKIUQVSjyTyY/dHyQq/RSDWjdTHUcIsyeFihBCVKED6UV83+o2OoR6MthKGq2FuFnyWySEEFUo7kwWAJH+boqTCGEZpEVFCCGqkOPyP2hS6kKUT6TqKEJYBJ2maZrqEJWVnZ2Nm5sbWVlZuLrKfBpCCLWK8gvB1QW70hLO7D5I/eimqiMJYZJu5PtbLv0IIUQVSd64A7vSErLsnfFr2UR1HCEsghQqQghRRdL/3ARAcoMIdNKRVogqIb9JQghRRax27AAgt5UM9CZEVZFCRQghqki9Q/sBcOwsA70JUVWkUBFCiCpwKfksARdSAAjq3VVtGCEsiBQqQghRBU6tWg9AilcA7oG+asMIYUFkHBUhhKgCG70b88GQN+ge4MRo1WGEsCDSoiKEEFVg58ViNoW0wmbQ3aqjCGFRlBYqn3/+OVFRUbi6uuLq6kpMTAwrVqxQGUkIIW6Y0agRm3IJgJYB7kqzCGFplBYq/v7+vPvuu+zevZvdu3fTvXt3Bg4cSHx8vMpYQghxQ5L3HOTxlV/S69Qewuu5qI4jhEVR2kdlwIABFZ6//fbbfP7552zfvp2mTWXoaSGEeUj7YzWP7VhIQsYxbKxfUR1HCItiMp1pS0tL+eWXX8jLyyMmJuaK6xgMBgwGQ/nz7OzsmoonhBBXpW3fDkBW82jFSYSwPMo708bFxeHs7Ixer2fMmDEsXryYiIiIK647ZcoU3Nzcyh8BAQE1nFYIIS7nFb8PAH2nDoqTCGF5lM+eXFRURHJyMpcuXWLRokV8+eWXbNiw4YrFypVaVAICAmT2ZCGEMtnnL+Lk64O1ZuTC4eN4NW6gOpIQJu9GZk9WfunHzs6OsLAwAFq3bs2uXbv46KOPmDVr1mXr6vV69Hp9TUcUQoirSlqyhuaakTMevtSXIkWIKqf80s+/aZpWodVECCFMWf669QCkRslEhEJUB6UtKi+88AJ9+/YlICCAnJwcFixYwPr161m5cqXKWEIIcd1sE/43nEKnW9QGEcJCKS1Uzp8/z4gRIzh79ixubm5ERUWxcuVKevbsqTKWEEJcl8LiUu697QXqtz/NNyP7qY4jhEVSWqh89dVXKg8vhBA3ZX/KJYqMGrlBDQgMra86jhAWyeT6qAghhLnYdTIDgLbBHuh0OsVphLBMyu/6EUIIc9XipQl8mpFNacSLqqMIYbGkUBFCiEooMRTRcs96nIoKOO4r4zgJUV3k0o8QQlTCybVbcSoqIFvvRHDXdqrjCGGxpFARQohKuLhyLQBJ4S2wtpXGaSGqixQqQghRCfqtWwDIbyfz+whRnaRQEUKIG1RiKCI0fhcAngP6KE4jhGWT9kohLFRG0mlSVm8kraCEPY3bciHHQInRyKC572FrZwP+/jg0b4Z/7y54hshM5Dfi2Ir1hBvyyLJ3JrR3Z9VxhLBoUqgIYSEKc/I4/N2vFP26GL/9O/FPP4MHsNM/gi+GTy1f78Utq/HOy6yw7Qm/UM5370vA+IfxbxtVw8nNT8LxNAp9G1ESGERr6Z8iRLWS3zAhzJimaew+mYHxgQeJ3LKKFsWFFV4/5R1IQcNwRnUIxsfVHjsbK44UTeT4mdPYJZ/CM+kowedP0iD1OA3mzeTQqqWMfncBj3VrSNsQD0VnZfoWOIey8/5pvHV7BK1VhxHCwkmhIoQZys3J5+f95/lhZzLH0nL5IjUNx+JCzrl5capTT+wH3kZwv1sJqu9NENDlnxt3eqXCvjKSTnN83iL0Py/g65BO/HX0In8dvUivMA9eamZPYPuWNXlqJi/PUMK+5LIWqU6NvBWnEcLySWdaIcxIRtJptg1/nKJ6fnw970+OpeXiYGvN4Yef5PDiVfhknKPd0vk0f/ge3Opf35eoR4g/bV6eQFTcNiZ+/Rr3tA3ExkpH/QVzqdepLdsfepoSQ1E1n5n52LfnCPr8PPzrOBDk6ag6jhAWT6dpmqY6RGVlZ2fj5uZGVlYWrq4yMqSwXOlJKSROfJHmy3/GocQAwHc9RqB7803uaOGHi71tlR7v+IVczt8+mA7bVwJwJKQp7ksX4xPRsEqPY4623zGS1kvmsfaecfSZ/5HqOEKYpRv5/pYWFSFMWPb5i2wb/jgOjRvRfsn3OJQYSAwMZ9/0Lxm+/GtGtA+q8iIFINTLmZgty9j1xgyy7Z1pnBSPTdu2JCz4o8qPZW58dm7GRjPi1aKJ6ihC1ArSoiKECSosLuX7jYn0v7sLfllpABwNbILh1ddoNmoQOqua+xvjzJ54Cm+/g9DUY5TorNjz3Nu0m/J8jR3flFw8dpK6DUMAyDx5hjpBfooTCWGepEVFCDNVbChi/o5TdHn/L95ec5yFEd045RPEvulf0jDpIJEPDKnRIgWgfnRT/OL3srtjX2w0I63fe5EfvluNGf+NU2knFywB4Jh/QylShKghctePECbAWFLK3ve/wPfDd/ij1zjOB0ZR390B/w/ewr9NMEGKx+pwcHcheuNSto0Yx28ZNvyUUMzJFYeZ3DccnU6nNFtN0i1fDsCFDl0JU5xFiNpCChUhFNKMRg589RPOr71C69RjADy+93f6jB3GPe0C0dtYK074/3RWVsTM/4z4TSdg2SFmbzyBdV4uz94dXeOtPCqUGIpouK9sfp86Q+9SnEaI2sPy/3cRwkQdXriCQ41b0fyRewlNPUaO3pFtoyYSvWUlozqGmFSR8k8P3dKAd++KxDsnnTsfG8SOkU+ojlQjji5ehWthLpmOrjQc0EN1HCFqDWlREaKGHT6XTep9D9F93S8AFNrYETvwPsKnvUVMoK/idNdnWNtA/H87R6P0ZJj3KdtdXGj/2RTVsarVajyY3+tx2vg4cIcMmy9EjZEWFSFqyIkLuTz5Uyx9P9rEEodASnRW7Oh5N1kH4mm/8CvczaRI+Vund55l20NPA9D+83fZPeVTxYmq17LUEua37If100+pjiJErSKFihDV7OSGnezu2Je59z3H4n1n0DQoGTqU1G17abd6IT5NzLdbZsycD9h+5ygAol6eSMJPy9QGqiYpGfkkpuVibaWjcyMv1XGEqFWk/VKIanJ8zRayXnqVFjv/JBgNXxcvzg+9j/G9Ioj0d1Mdr8q0/flL9sacotXuv/AbfQ8pQRsIsLD5gY7MnMv9e+K40Os23ByqfoA9IcTVSYuKEFVIMxo5+P1i9rfoRGivTrTauQ4rNPa26U7BwkXMfiDGoooUACsba5qs+Z2jQU1wL8ghY/gosguLVceqUgHffM4ba2cx6twe1VGEqHWkUBGiChhKSvlldwoLewyn2f130Xz/Fozo2BPTm6S/ttNq5zrCet2iOma1cXB3wWPdSv5q2olHez/FkwtiMRotY0C4tEPHaZwUD0CDh+9TnEaI2kcu/QhxE87sOciSg+f5KkXjYq6BFv5t6G+7mLied+L3yvNEt2uuOmKNqRsaiOfKP8j4YhvrDqfx0bpEnuzZSHWsm5Y0Zx7ewOGQZoSHh6qOI0StIy0qQtygovxC9nwwm7iIttRvHYnLjA+5mGugnqs9vR8YSFHyadot+5GAWlSk/C3K352372gGwJHPvmHfp98rTnTznJeVDZuf1XeA4iRC1E7SoiLEdTAWl3Do52XkffM9jTevIrowt2w5OhraFPHFfa24tYkPttZS+w9uHUDRb0sY/tsUcpc7ktyqKYExrVTHqpSLx04SnhgLQMBDctlHCBWkUBHiKkpKjew5lcmahPMMGDeE5qfiy19Lc/HkxG2DCZw0nnYtIxSmNE1DXnqIhJ+/JCIxlot33k1e/D6cPN1Vx7phxz7+ivaaseyyj/w7C6GEFCpC/EP2+YskfreQkmXLGX/LI6QVl024V8c3gpBzJznSqRcOI0fQZNgAvGV00quytdfjvXwxF1pFE3z+JHsGDKPV5uVmNyfQmYTjFFtZk3XXYNVRhKi1dJoZz9WenZ2Nm5sbWVlZuLq6qo4jzFBhTh7Hf19D9qq1uG3fTNiJeOyMJQCMGvQasZExdG/sTf8gRzpF+qN3clSc2LwcXriC0KEDsDWWsn3CK7Sf8brqSNftWFouPaZtwLMwh9XP9cDTt67qSEJYjBv5/pY/CUWtkpFrYP+ZLGKTL6EtWsjYL1+jaWnFMT+SvQJIvaUHT43pQ0TXNthIv5NKCx/Ul+3jXqD9x28S/cnbHO7UnvBBfVXHui6/x54BoHmLUClShFBIChVhkTSjkfRjpzi7dS95u/Zgu3cPvkfj+Cz6Dua16g9Ao2I3niot5qKzByej2mLs0pX6d/UjsHUkgYrzW5J2019jz84dRG9fzfpZP+PZuzteLnrVsa6ptLiEXau2g21dBrbwUx1HiFpNaaEyZcoUfv31Vw4fPoyDgwMdOnTgvffeo3HjxipjCTNSYiji3LlMThVbc/xCLmlxR+j3/nP4nTlB3YIc/v13cIuzR9nqNYQW/u60CmxK8kMxBLRrQV0z6zthTnRWVoQv/YmXJ33G997NWf/jXuY92M6kW6riv13IgmmjWBfekY5vblAdR4haTWmhsmHDBsaOHUubNm0oKSnhxRdfpFevXiQkJODk5KQymjARuYYSzl/MpuCvjRSeTKbk9Bl0J5NwOJ2Mx7kUfDLPsyGqJy/2HgeAkyGfZ47tB6BUZ0Vq3fpcCArD0KIVLrd0oGfPWxjk+89J5YJr/qRqISdPd0a++wS/ztzM9hMZvL/yEJP7N1Ud66pKv5gFgFPDEOxtrRWnEaJ2U1qorFy5ssLzuXPn4u3tzZ49e+jcubOiVKK6aJpGrqGES/nFZGblwZ9/UpR2kdILFzGmp2OVno71pUwcLqaxNyCC9zoMJ9dQgkNRIYemD7rqfgOyLxDq5USwpxON6rmw0+8LPJtHUD+mFQEuTgTU4DmKqwvzdub9wc15ddZauj/2PPuemkjLiQ+qjnWZC0dOELl3IwD1nh6vOI0QwqT6qGRlZQHg4eFxxdcNBgMGg6H8eXZ2do3ksnSa0UhJUTGG3HyK8wowoKPQyRVDiZGiAgO2e3ZRkl9AaUEhxoICSgsKMBYY0AoLyPAJ4FiLDuQWlVCUnUP/mW9gk5+LTUEedvl56AvzsS/Mx8FQwKpGMUzqOwEAu5Jijn447KqZLhRBbnTZ3Tc2Ls4c9m9MibMzhZ7eFAcEYd0wDKcmDanbvAmdGjVgnc0//urtE16t75eovH6RvjhnbKddykFyn3uClPatTG6m5WPvfkKMZuRQaBRNurRVHUeIWs9kChVN03jqqafo1KkTzZo1u+I6U6ZM4fXXq//2xkNns9m2dDNN/lwCmgZGDdDQGY2gaWhGjUMde3K6cQs0NDxSkmi77Afg/9f9ezudpnGgQy8SI9tj1DQ8ziXTY9Gcste1/+2z7A1AZzSyp31P9rfujoZGnbRUBs2fBhrl+9QZjejQ0JUa2d76Vv6M6UepUcP94lkmzXkZK2MJ1qWlWBlLsSotxdpY9vMfrXozq9sISowaHpcusnTmA9gYS7E2GrHWjNgCf09eP79Fn/JLKW4FOez/+J6rvle/RXThwwFlPUFsS4t5dfOyq67rmZsJgL2tFXXcXDgUFIHR3gGDmzvF7h4Y69RB5+mJjb8fHuGN+bNTDN6u9jjrbeD1wzfzTypMSMzc6STs3kpEYiwX7ryb/IR9ONYxjRmlDXn5NFz4HQB5o0yvtUeI2shkCpVx48Zx4MABNm/efNV1Jk+ezFNPPVX+PDs7m4CAqm/YP3o+h60rt/PAr7Ouus7SfEfmXyz7z7XDyf2MX/7jVdddVerOT4b6ALQ6c4TJG/646rrrbbxYpm8CQHjaKd7es/6q625xqs92n/YABGZm0ij50FXXtb2UycXcIgBsio04Fhuuuq7eWIqTnTV6W2tcHVw47VmfYls7Suz0lNjaUWpnR4mtHqOdHcYmrbinbQCOdjY46W3Ylj8ZKxdnrN1csXFxxcbdFX0dN/QebjTz8eJwgN//X/OfHH/VDMJy2ert8F76KxejWxNyLondA+4heuNSkxgMbv/UL2ibm0GaiydRTz2iOo4QAhMZ8G38+PH89ttvbNy4kZCQkOverroGfIs7ncXGX/8kes0i0OlAp0OzsgJ06KzKnh/p2JOz4S3Q6aDOuRQiV/0K/3sNndX/frYCnY6UVh240Kysedsp/TyN1ywp3w86HVhZlf98oWlL0pu2QKfTYZ+dSfCfywAdWOnQWVmDDtDp0NnaktuwCblNo7Cx0mFjKMBr91Z0NrZY2dpiZWuDla0NOhsbrGxt0Xy80QICsLHSYa1p6FNPY2Vjg7WdDVZ2ttjY2mLn4oydkwPWNtJ5UFS/hJ+W0eie27HRjGx/6nXaf/iK0jyaprE3vC3RR3ez/dFnaf/Fe0rzCGHJbuT7W2mhomka48ePZ/Hixaxfv56GDRve0PYyMq0Q5m37+JdoP/NtiqxsOLFwGeF39lKWZf2RNB6Zs5VBiZt47ovncatwd5gQoirdyPe30rbWsWPHMm/ePH744QdcXFw4d+4c586do6CgQGUsIUQNaffRG+xteyvJ7vV4bcNpLuZe/ZJkddI0jelrjlJkY4vDww9KkSKECVHaoqLT6a64fO7cuYwaNeo/t5cWFSHMX+7FTIbN2sbBHI2YBp58/2DbGh8MbtPa3YxedQZbez0bn+1m8iPnCmHuzKZFRdO0Kz6up0gRQlgG57p1mPHwLTjaWbPtRDpz5v1Vo8c3lpRSf+Qw1n05hud98qRIEcLEqO9mL4So9cK8XZg6KIoHdy7moQd6s+/juTV27N1vzKBB6nHqFORwx52dauy4QojrI4WKEMIk3BblR0+XImyNpTSaNJbEZdXfspJ1+hwNP3wTgIQHxuNW36fajymEuDFSqAghTEb0T3M42KQ1TkUF1B1yJ8nb9lbr8Q4/+AR18rM4WS+Y6GmvVeuxhBCVI4WKEMJk2NrrCd64msSAxtTJz8KuX1/OJyRWy7HivllEu9W/AJA37SNs7aVvihCmSAoVIYRJca5bB88Na0nxCqDepTSKO3cldX/VTqGQkXQa3/FlI8/u6D2YpvfcXqX7F0JUHSlUhBAmxyPEH5u1q0n18MU/PZUFL83k+IXcKtl3SamRl5YkEOsTykmfYKIWfFUl+xVCVA8pVIQQJsk3KhybzZv4dOBYPm7al6GztrEvOfOm9qlpGi8uPsjys8WMG/YaRWvX4eDuUkWJhRDVQQoVIYTJ8m4SyrB5H9LUz5WLuUU8MmMN296eWal9GUtK+f2Zqfy86xRWOvjk3mgaNWtQxYmFEFVNChUhhEnzdNaz4JH29G7syYeL3yXmpfHsbdeTjKTT172Pgks57LulH3dMe57Jf83ljYHN6BkhtyILYQ6kUBFCmDwXe1s+H9EG265dKLayptXOtVhHNGHbo8+Sn5l11e00o5G4r38mI7Qx0dtXU2RlQ8vbu3Jf+6AaTC+EuBlK5/q5WTLXjxC1z7GVG7B68EEapB4HIM/OgYQOPcl56FG8u3ZEb2NF3v6DFP7+B16/LyT0TNntzefdvLgwczbN7rtDYXohBNzY97dNDWUSQogqEdanC6UnD7PrnZn4fvI+/umptFm/hCdcGrMkruzvrgd3/cbLf34JgMHaln19h9D0q49o5u2pMroQohKkUBFCmB1rWxvavDoR7eUnSPhlBdnzF1DSrh11NT1GTSO5cQv2XeqCoeMthD/zOO0DfVVHFkJUklz6EUIIIUSNupHvb+lMK4QQQgiTJYWKEEIIIUyWFCpCCCGEMFlSqAghhBDCZEmhIoQQQgiTJYWKEEIIIUyWFCpCCCGEMFlSqAghhBDCZEmhIoQQQgiTJYWKEEIIIUyWFCpCCCGEMFlSqAghhBDCZEmhIoQQQgiTJYWKEEIIIUyWjeoAN0PTNKBsumghhBBCmIe/v7f//h6/FrMuVHJycgAICAhQnEQIIYQQNyonJwc3N7drrqPTrqecMVFGo5HU1FRcXFzQ6XRVuu/s7GwCAgJISUnB1dW1SvdtCuT8zJ+ln6Olnx9Y/jnK+Zm/6jpHTdPIycnBz88PK6tr90Ix6xYVKysr/P39q/UYrq6uFvsBBDk/S2Dp52jp5weWf45yfuavOs7xv1pS/iadaYUQQghhsqRQEUIIIYTJkkLlKvR6Pa+++ip6vV51lGoh52f+LP0cLf38wPLPUc7P/JnCOZp1Z1ohhBBCWDZpURFCCCGEyZJCRQghhBAmSwoVIYQQQpgsKVSEEEIIYbKkULkOt99+O4GBgdjb2+Pr68uIESNITU1VHatKnDx5kgcffJCQkBAcHBwIDQ3l1VdfpaioSHW0KvX222/ToUMHHB0dcXd3Vx3npn322WeEhIRgb29PdHQ0mzZtUh2pymzcuJEBAwbg5+eHTqfjt99+Ux2pSk2ZMoU2bdrg4uKCt7c3d9xxB0eOHFEdq0p9/vnnREVFlQ8SFhMTw4oVK1THqhZTpkxBp9MxceJE1VGqzGuvvYZOp6vwqFevnrI8Uqhch27duvHzzz9z5MgRFi1axPHjxxk0aJDqWFXi8OHDGI1GZs2aRXx8PNOnT+eLL77ghRdeUB2tShUVFTF48GAee+wx1VFu2k8//cTEiRN58cUX2bdvH7fccgt9+/YlOTlZdbQqkZeXR/PmzZk5c6bqKNViw4YNjB07lu3bt7NmzRpKSkro1asXeXl5qqNVGX9/f9599112797N7t276d69OwMHDiQ+Pl51tCq1a9cuZs+eTVRUlOooVa5p06acPXu2/BEXF6cujCZu2O+//67pdDqtqKhIdZRqMXXqVC0kJER1jGoxd+5czc3NTXWMm9K2bVttzJgxFZaFh4drzz//vKJE1QfQFi9erDpGtUpLS9MAbcOGDaqjVKs6depoX375peoYVSYnJ0dr2LChtmbNGq1Lly7ahAkTVEeqMq+++qrWvHlz1THKSYvKDcrIyGD+/Pl06NABW1tb1XGqRVZWFh4eHqpjiCsoKipiz5499OrVq8LyXr16sXXrVkWpxM3IysoCsNjfudLSUhYsWEBeXh4xMTGq41SZsWPH0r9/f3r06KE6SrVITEzEz8+PkJAQhg0bxokTJ5RlkULlOj333HM4OTnh6elJcnIyv//+u+pI1eL48eN88sknjBkzRnUUcQUXL16ktLQUHx+fCst9fHw4d+6colSisjRN46mnnqJTp040a9ZMdZwqFRcXh7OzM3q9njFjxrB48WIiIiJUx6oSCxYsYO/evUyZMkV1lGrRrl07vvvuO1atWsWcOXM4d+4cHTp0ID09XUmeWluoXKmz0L8fu3fvLl9/0qRJ7Nu3j9WrV2Ntbc3999+PZsKD+t7o+QGkpqbSp08fBg8ezEMPPaQo+fWrzDlaCp1OV+G5pmmXLROmb9y4cRw4cIAff/xRdZQq17hxY2JjY9m+fTuPPfYYI0eOJCEhQXWsm5aSksKECROYN28e9vb2quNUi759+3L33XcTGRlJjx49WLZsGQDffvutkjw2So5qAsaNG8ewYcOuuU5wcHD5z3Xr1qVu3bo0atSIJk2aEBAQwPbt2022KfNGzy81NZVu3boRExPD7Nmzqzld1bjRc7QEdevWxdra+rLWk7S0tMtaWYRpGz9+PEuWLGHjxo34+/urjlPl7OzsCAsLA6B169bs2rWLjz76iFmzZilOdnP27NlDWloa0dHR5ctKS0vZuHEjM2fOxGAwYG1trTBh1XNyciIyMpLExEQlx6+1hcrfhUdl/N2SYjAYqjJSlbqR8ztz5gzdunUjOjqauXPnYmVlHg1tN/NvaK7s7OyIjo5mzZo13HnnneXL16xZw8CBAxUmE9dL0zTGjx/P4sWLWb9+PSEhIaoj1QhN00z6/8zrdeutt152B8zo0aMJDw/nueees7giBcq+6w4dOsQtt9yi5Pi1tlC5Xjt37mTnzp106tSJOnXqcOLECV555RVCQ0NNtjXlRqSmptK1a1cCAwP54IMPuHDhQvlrKu+br2rJyclkZGSQnJxMaWkpsbGxAISFheHs7Kw23A166qmnGDFiBK1bty5vAUtOTraYfkW5ubkcO3as/HlSUhKxsbF4eHgQGBioMFnVGDt2LD/88AO///47Li4u5a1jbm5uODg4KE5XNV544QX69u1LQEAAOTk5LFiwgPXr17Ny5UrV0W6ai4vLZf2J/u6/aCn9jJ555hkGDBhAYGAgaWlpvPXWW2RnZzNy5Eg1gVTecmQODhw4oHXr1k3z8PDQ9Hq9FhwcrI0ZM0Y7ffq06mhVYu7cuRpwxYclGTly5BXP8a+//lIdrVI+/fRTLSgoSLOzs9NatWplUbe2/vXXX1f8txo5cqTqaFXiar9vc+fOVR2tyjzwwAPln08vLy/t1ltv1VavXq06VrWxtNuThw4dqvn6+mq2traan5+fdtddd2nx8fHK8ug0zYR7hAohhBCiVjOPzghCCCGEqJWkUBFCCCGEyZJCRQghhBAmSwoVIYQQQpgsKVSEEEIIYbKkUBFCCCGEyZJCRQghhBAmSwoVIYQQQpgsKVSEsEBdu3Zl4sSJqmNcUXp6Ot7e3pw8eRKA9evXo9PpuHTpUrUet7LH+eabb3B3d7+hbdq0acOvv/56Q9sIIa5MChUhxH86e/Ys9957L40bN8bKyuqqRdCiRYuIiIhAr9cTERHB4sWLL1tnypQpDBgwwOJmtv6nl19+meeffx6j0ag6ihBmTwoVIcR/MhgMeHl58eKLL9K8efMrrrNt2zaGDh3KiBEj2L9/PyNGjGDIkCHs2LGjfJ2CggK++uorHnrooZqKrkT//v3Jyspi1apVqqMIYfakUBHCwmVmZnL//fdTp04dHB0d6du3L4mJiRXWmTNnDgEBATg6OnLnnXcybdq0Cpc7goOD+eijj7j//vtxc3O74nFmzJhBz549mTx5MuHh4UyePJlbb72VGTNmlK+zYsUKbGxsrjnzeHp6Ovfccw/+/v44OjoSGRnJjz/+WGGdrl27Mn78eCZOnEidOnXw8fFh9uzZ5OXlMXr0aFxcXAgNDWXFihWX7X/Lli00b94ce3t72rVrR1xcXIXXv/nmGwIDA8vfi/T09AqvHz9+nIEDB+Lj44OzszNt2rRh7dq1FdaxtramX79+l+UWQtw4KVSEsHCjRo1i9+7dLFmyhG3btqFpGv369aO4uBgo++IeM2YMEyZMIDY2lp49e/L222/f8HG2bdtGr169Kizr3bs3W7duLX++ceNGWrdufc39FBYWEh0dzdKlSzl48CCPPPIII0aMqNAyA/Dtt99St25ddu7cyfjx43nssccYPHgwHTp0YO/evfTu3ZsRI0aQn59fYbtJkybxwQcfsGvXLry9vbn99tvL34sdO3bwwAMP8PjjjxMbG0u3bt146623Kmyfm5tLv379WLt2Lfv27aN3794MGDCA5OTkCuu1bduWTZs2Xd+bJ4S4OmXzNgshqs3f084fPXpUA7QtW7aUv3bx4kXNwcFB+/nnnzVNK5vSvX///hW2Hz58uObm5nbNff+bra2tNn/+/ArL5s+fr9nZ2ZU/HzhwoPbAAw9UWOevv/7SAC0zM/Oq59OvXz/t6aefrpChU6dO5c9LSko0JycnbcSIEeXLzp49qwHatm3bKhxnwYIF5eukp6drDg4O2k8//aRpmqbdc889Wp8+fSoce+jQoVd9L/4WERGhffLJJxWW/f7775qVlZVWWlp6zW2FENcmLSpCWLBDhw5hY2NDu3btypd5enrSuHFjDh06BMCRI0do27Zthe3+/fx66XS6Cs81TauwrKCgAHt7+2vuo7S0lLfffpuoqCg8PT1xdnZm9erVl7VYREVFlf9sbW2Np6cnkZGR5ct8fHwASEtLq7DdPy87eXh4VHgvDh06dNllqX8/z8vL49lnnyUiIgJ3d3ecnZ05fPjwZfkcHBwwGo0YDIZrnq8Q4tpsVAcQQlQfTdOuuvzvAuLfxcS1truWevXqce7cuQrL0tLSygsGgLp165KZmXnN/Xz44YdMnz6dGTNmEBkZiZOTExMnTqSoqKjCera2thWe63S6Csv+PqfrufPmn+/Ff5k0aRKrVq3igw8+ICwsDAcHBwYNGnRZvoyMDBwdHXFwcPjPfQohrk5aVISwYBEREZSUlFTo35Gens7Ro0dp0qQJAOHh4ezcubPCdrt3777hY8XExLBmzZoKy1avXk2HDh3Kn7ds2ZKEhIRr7mfTpk0MHDiQ++67j+bNm9OgQYPLOv/ejO3bt5f/nJmZydGjRwkPDwfK3q9/vv7v9f/ON2rUKO68804iIyOpV69e+Zgw/3Tw4EFatWpVZbmFqK2kUBHCgjVs2JCBAwfy8MMPs3nzZvbv3899991H/fr1GThwIADjx49n+fLlTJs2jcTERGbNmsWKFSsua2WJjY0lNjaW3NxcLly4QGxsbIWiY8KECaxevZr33nuPw4cP895777F27doKY6707t2b+Pj4a7aqhIWFsWbNGrZu3cqhQ4d49NFHL2upuRlvvPEG69at4+DBg4waNYq6detyxx13APDEE0+wcuVKpk6dytGjR5k5cyYrV668LN+vv/5KbGws+/fv5957771iq82mTZsu61wshLhxUqgIYeHmzp1LdHQ0t912GzExMWiaxvLly8svk3Ts2JEvvviCadOm0bx5c1auXMmTTz55WV+Sli1b0rJlS/bs2cMPP/xAy5Yt6devX/nrHTp0YMGCBcydO5eoqCi++eYbfvrppwr9YyIjI2ndujU///zzVfO+/PLLtGrVit69e9O1a1fq1atXXkhUhXfffZcJEyYQHR3N2bNnWbJkCXZ2dgC0b9+eL7/8kk8++YQWLVqwevVqXnrppQrbT58+nTp16tChQwcGDBhA7969L2s5OXPmDFu3bmX06NFVlluI2kqnVeZitBDCoj388MMcPny4Wm6vXb58Oc888wwHDx7Eysoy/1aaNGkSWVlZzJ49W3UUIcyedKYVQvDBBx/Qs2dPnJycWLFiBd9++y2fffZZtRyrX79+JCYmcubMGQICAqrlGKp5e3vzzDPPqI4hhEWQFhUhBEOGDGH9+vXk5OTQoEEDxo8fz5gxY1THEkIIKVSEEEIIYbos8wKxEEIIISyCFCpCCCGEMFlSqAghhBDCZEmhIoQQQgiTJYWKEEIIIUyWFCpCCCGEMFlSqAghhBDCZEmhIoQQQgiT9X+fZkvEqg05YAAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_75_0.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.model_selection import KFold\n",
|
||
"from sklearn.linear_model import Ridge\n",
|
||
"from sklearn.model_selection import cross_val_score\n",
|
||
"from sklearn.preprocessing import PolynomialFeatures\n",
|
||
"\n",
|
||
"# A seed just to ensure that the random numbers are the same for every run.\n",
|
||
"# Useful for eventual debugging.\n",
|
||
"np.random.seed(3155)\n",
|
||
"\n",
|
||
"# Generate the data.\n",
|
||
"nsamples = 100\n",
|
||
"x = np.random.randn(nsamples)\n",
|
||
"y = 3*x**2 + np.random.randn(nsamples)\n",
|
||
"\n",
|
||
"## Cross-validation on Ridge regression using KFold only\n",
|
||
"\n",
|
||
"# Decide degree on polynomial to fit\n",
|
||
"poly = PolynomialFeatures(degree = 6)\n",
|
||
"\n",
|
||
"# Decide which values of lambda to use\n",
|
||
"nlambdas = 500\n",
|
||
"lambdas = np.logspace(-3, 5, nlambdas)\n",
|
||
"\n",
|
||
"# Initialize a KFold instance\n",
|
||
"k = 5\n",
|
||
"kfold = KFold(n_splits = k)\n",
|
||
"\n",
|
||
"# Perform the cross-validation to estimate MSE\n",
|
||
"scores_KFold = np.zeros((nlambdas, k))\n",
|
||
"\n",
|
||
"i = 0\n",
|
||
"for lmb in lambdas:\n",
|
||
" ridge = Ridge(alpha = lmb)\n",
|
||
" j = 0\n",
|
||
" for train_inds, test_inds in kfold.split(x):\n",
|
||
" xtrain = x[train_inds]\n",
|
||
" ytrain = y[train_inds]\n",
|
||
"\n",
|
||
" xtest = x[test_inds]\n",
|
||
" ytest = y[test_inds]\n",
|
||
"\n",
|
||
" Xtrain = poly.fit_transform(xtrain[:, np.newaxis])\n",
|
||
" ridge.fit(Xtrain, ytrain[:, np.newaxis])\n",
|
||
"\n",
|
||
" Xtest = poly.fit_transform(xtest[:, np.newaxis])\n",
|
||
" ypred = ridge.predict(Xtest)\n",
|
||
"\n",
|
||
" scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)\n",
|
||
"\n",
|
||
" j += 1\n",
|
||
" i += 1\n",
|
||
"\n",
|
||
"\n",
|
||
"estimated_mse_KFold = np.mean(scores_KFold, axis = 1)\n",
|
||
"\n",
|
||
"## Cross-validation using cross_val_score from sklearn along with KFold\n",
|
||
"\n",
|
||
"# kfold is an instance initialized above as:\n",
|
||
"# kfold = KFold(n_splits = k)\n",
|
||
"\n",
|
||
"estimated_mse_sklearn = np.zeros(nlambdas)\n",
|
||
"i = 0\n",
|
||
"for lmb in lambdas:\n",
|
||
" ridge = Ridge(alpha = lmb)\n",
|
||
"\n",
|
||
" X = poly.fit_transform(x[:, np.newaxis])\n",
|
||
" estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)\n",
|
||
"\n",
|
||
" # cross_val_score return an array containing the estimated negative mse for every fold.\n",
|
||
" # we have to the the mean of every array in order to get an estimate of the mse of the model\n",
|
||
" estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n",
|
||
"\n",
|
||
" i += 1\n",
|
||
"\n",
|
||
"## Plot and compare the slightly different ways to perform cross-validation\n",
|
||
"\n",
|
||
"plt.figure()\n",
|
||
"\n",
|
||
"plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n",
|
||
"plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')\n",
|
||
"\n",
|
||
"plt.xlabel('log10(lambda)')\n",
|
||
"plt.ylabel('mse')\n",
|
||
"\n",
|
||
"plt.legend()\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f501c9cf",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"More examples of the application of cross-validation follow here."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "30e1e320",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n",
|
||
" plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_77_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Common imports\n",
|
||
"import os\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
|
||
"from sklearn.metrics import mean_squared_error\n",
|
||
"from sklearn.model_selection import KFold\n",
|
||
"from sklearn.model_selection import cross_val_score\n",
|
||
"\n",
|
||
"\n",
|
||
"# Where to save the figures and data files\n",
|
||
"PROJECT_ROOT_DIR = \"Results\"\n",
|
||
"FIGURE_ID = \"Results/FigureFiles\"\n",
|
||
"DATA_ID = \"DataFiles/\"\n",
|
||
"\n",
|
||
"if not os.path.exists(PROJECT_ROOT_DIR):\n",
|
||
" os.mkdir(PROJECT_ROOT_DIR)\n",
|
||
"\n",
|
||
"if not os.path.exists(FIGURE_ID):\n",
|
||
" os.makedirs(FIGURE_ID)\n",
|
||
"\n",
|
||
"if not os.path.exists(DATA_ID):\n",
|
||
" os.makedirs(DATA_ID)\n",
|
||
"\n",
|
||
"def image_path(fig_id):\n",
|
||
" return os.path.join(FIGURE_ID, fig_id)\n",
|
||
"\n",
|
||
"def data_path(dat_id):\n",
|
||
" return os.path.join(DATA_ID, dat_id)\n",
|
||
"\n",
|
||
"def save_fig(fig_id):\n",
|
||
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
|
||
"\n",
|
||
"infile = open(data_path(\"EoS.csv\"),'r')\n",
|
||
"\n",
|
||
"# Read the EoS data as csv file and organize the data into two arrays with density and energies\n",
|
||
"EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n",
|
||
"EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n",
|
||
"EoS = EoS.dropna()\n",
|
||
"Energies = EoS['Energy']\n",
|
||
"Density = EoS['Density']\n",
|
||
"# The design matrix now as function of various polytrops\n",
|
||
"\n",
|
||
"Maxpolydegree = 30\n",
|
||
"X = np.zeros((len(Density),Maxpolydegree))\n",
|
||
"X[:,0] = 1.0\n",
|
||
"estimated_mse_sklearn = np.zeros(Maxpolydegree)\n",
|
||
"polynomial = np.zeros(Maxpolydegree)\n",
|
||
"k =5\n",
|
||
"kfold = KFold(n_splits = k)\n",
|
||
"\n",
|
||
"for polydegree in range(1, Maxpolydegree):\n",
|
||
" polynomial[polydegree] = polydegree\n",
|
||
" for degree in range(polydegree):\n",
|
||
" X[:,degree] = Density**(degree/3.0)\n",
|
||
" OLS = LinearRegression(fit_intercept=False)\n",
|
||
"# loop over trials in order to estimate the expectation value of the MSE\n",
|
||
" estimated_mse_folds = cross_val_score(OLS, X, Energies, scoring='neg_mean_squared_error', cv=kfold)\n",
|
||
"#[:, np.newaxis]\n",
|
||
" estimated_mse_sklearn[polydegree] = np.mean(-estimated_mse_folds)\n",
|
||
"\n",
|
||
"plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n",
|
||
"plt.xlabel('Polynomial degree')\n",
|
||
"plt.ylabel('log10[MSE]')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "383e5c2a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Note that we have kept the intercept in the first column of design matrix $\\boldsymbol{X}$. When we call the corresponding **Scikit-Learn** function we need thus to set the intercept to **False**. Libraries like **Scikit-Learn** normally scale the design matrix and do not fit intercept. See the discussions below."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1f9cd409",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## More on Rescaling data\n",
|
||
"\n",
|
||
"We end this chapter by adding some words on scaling and how to deal with the intercept for regression cases.\n",
|
||
"\n",
|
||
"When you are comparing your own code with for example **Scikit-Learn**'s\n",
|
||
"library, there are some technicalities to keep in mind. The examples\n",
|
||
"here demonstrate some of these aspects with potential pitfalls.\n",
|
||
"\n",
|
||
"The discussion here focuses on the role of the intercept, how we can\n",
|
||
"set up the design matrix, what scaling we should use and other topics\n",
|
||
"which tend confuse us.\n",
|
||
"\n",
|
||
"The intercept can be interpreted as the expected value of our\n",
|
||
"target/output variables when all other predictors are set to zero.\n",
|
||
"Thus, if we cannot assume that the expected outputs/targets are zero\n",
|
||
"when all predictors are zero (the columns in the design matrix), it\n",
|
||
"may be a bad idea to implement a model which penalizes the intercept.\n",
|
||
"Furthermore, in for example Ridge and Lasso regression, the default solutions\n",
|
||
"from the library **Scikit-Learn** (when not shrinking $\\beta_0$) for the unknown parameters\n",
|
||
"$\\boldsymbol{\\beta}$, are derived under the assumption that both $\\boldsymbol{y}$ and\n",
|
||
"$\\boldsymbol{X}$ are zero centered, that is we subtract the mean values.\n",
|
||
"\n",
|
||
"If our predictors represent different scales, then it is important to\n",
|
||
"standardize the design matrix $\\boldsymbol{X}$ by subtracting the mean of each\n",
|
||
"column from the corresponding column and dividing the column with its\n",
|
||
"standard deviation. Most machine learning libraries do this as a default. This means that if you compare your code with the results from a given library,\n",
|
||
"the results may differ. \n",
|
||
"\n",
|
||
"The\n",
|
||
"[Standardscaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n",
|
||
"function in **Scikit-Learn** does this for us. For the data sets we\n",
|
||
"have been studying in our various examples, the data are in many cases\n",
|
||
"already scaled and there is no need to scale them. You as a user of different machine learning algorithms, should always perform a\n",
|
||
"survey of your data, with a critical assessment of them in case you need to scale the data.\n",
|
||
"\n",
|
||
"If you need to scale the data, not doing so will give an *unfair*\n",
|
||
"penalization of the parameters since their magnitude depends on the\n",
|
||
"scale of their corresponding predictor.\n",
|
||
"\n",
|
||
"Suppose as an example that you \n",
|
||
"you have an input variable given by the heights of different persons.\n",
|
||
"Human height might be measured in inches or meters or\n",
|
||
"kilometers. If measured in kilometers, a standard linear regression\n",
|
||
"model with this predictor would probably give a much bigger\n",
|
||
"coefficient term, than if measured in millimeters.\n",
|
||
"This can clearly lead to problems in evaluating the cost/loss functions.\n",
|
||
"\n",
|
||
"Keep in mind that when you transform your data set before training a model, the same transformation needs to be done\n",
|
||
"on your eventual new data set before making a prediction. If we translate this into a Python code, it would could be implemented as follows"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "eab81633",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'\\n#Model training, we compute the mean value of y and X\\ny_train_mean = np.mean(y_train)\\nX_train_mean = np.mean(X_train,axis=0)\\nX_train = X_train - X_train_mean\\ny_train = y_train - y_train_mean\\n\\n# The we fit our model with the training data\\ntrained_model = some_model.fit(X_train,y_train)\\n\\n\\n#Model prediction, we need also to transform our data set used for the prediction.\\nX_test = X_test - X_train_mean #Use mean from training data\\ny_pred = trained_model(X_test)\\ny_pred = y_pred + y_train_mean\\n'"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"#Model training, we compute the mean value of y and X\n",
|
||
"y_train_mean = np.mean(y_train)\n",
|
||
"X_train_mean = np.mean(X_train,axis=0)\n",
|
||
"X_train = X_train - X_train_mean\n",
|
||
"y_train = y_train - y_train_mean\n",
|
||
"\n",
|
||
"# The we fit our model with the training data\n",
|
||
"trained_model = some_model.fit(X_train,y_train)\n",
|
||
"\n",
|
||
"\n",
|
||
"#Model prediction, we need also to transform our data set used for the prediction.\n",
|
||
"X_test = X_test - X_train_mean #Use mean from training data\n",
|
||
"y_pred = trained_model(X_test)\n",
|
||
"y_pred = y_pred + y_train_mean\n",
|
||
"\"\"\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0ca51a54",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Let us try to understand what this may imply mathematically when we\n",
|
||
"subtract the mean values, also known as *zero centering*. For\n",
|
||
"simplicity, we will focus on ordinary regression, as done in the above example.\n",
|
||
"\n",
|
||
"The cost/loss function for regression is"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fa3a4110",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"C(\\beta_0, \\beta_1, ... , \\beta_{p-1}) = \\frac{1}{n}\\sum_{i=0}^{n} \\left(y_i - \\beta_0 - \\sum_{j=1}^{p-1} X_{ij}\\beta_j\\right)^2,.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9f499302",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Recall also that we use the squared value. This expression can lead to an\n",
|
||
"increased penalty for higher differences between predicted and\n",
|
||
"output/target values.\n",
|
||
"\n",
|
||
"What we have done is to single out the $\\beta_0$ term in the\n",
|
||
"definition of the mean squared error (MSE). The design matrix $X$\n",
|
||
"does in this case not contain any intercept column. When we take the\n",
|
||
"derivative with respect to $\\beta_0$, we want the derivative to obey"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b1f7ba52",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\frac{\\partial C}{\\partial \\beta_j} = 0,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7ff75b7f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"for all $j$. For $\\beta_0$ we have"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eac37d3c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\frac{\\partial C}{\\partial \\beta_0} = -\\frac{2}{n}\\sum_{i=0}^{n-1} \\left(y_i - \\beta_0 - \\sum_{j=1}^{p-1} X_{ij} \\beta_j\\right).\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e653464",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Multiplying away the constant $2/n$, we obtain"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8afa60a1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\sum_{i=0}^{n-1} \\beta_0 = \\sum_{i=0}^{n-1}y_i - \\sum_{i=0}^{n-1} \\sum_{j=1}^{p-1} X_{ij} \\beta_j.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "055b7975",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Let us specialize first to the case where we have only two parameters $\\beta_0$ and $\\beta_1$.\n",
|
||
"Our result for $\\beta_0$ simplifies then to"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a2bf6e5d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"n\\beta_0 = \\sum_{i=0}^{n-1}y_i - \\sum_{i=0}^{n-1} X_{i1} \\beta_1.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e15afbd5",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We obtain then"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d2f9a064",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\beta_1\\frac{1}{n}\\sum_{i=0}^{n-1} X_{i1}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8e8035d8",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"If we define"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0bbcef44",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mu_{\\boldsymbol{x}_1}=\\frac{1}{n}\\sum_{i=0}^{n-1} X_{i1},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "919462c2",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"and the mean value of the outputs as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3a0899ec",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mu_y=\\frac{1}{n}\\sum_{i=0}^{n-1}y_i,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9b255ec5",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"we have"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7716aca1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\beta_0 = \\mu_y - \\beta_1\\mu_{\\boldsymbol{x}_1}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "24863f48",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"In the general case with more parameters than $\\beta_0$ and $\\beta_1$, we have"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c10f940",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\frac{1}{n}\\sum_{i=0}^{n-1}\\sum_{j=1}^{p-1} X_{ij}\\beta_j.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7afefe95",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We can rewrite the latter equation as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a2e7bc1a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\sum_{j=1}^{p-1} \\mu_{\\boldsymbol{x}_j}\\beta_j,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5ded7d78",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where we have defined"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bcac7366",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mu_{\\boldsymbol{x}_j}=\\frac{1}{n}\\sum_{i=0}^{n-1} X_{ij},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bafe8156",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"the mean value for all elements of the column vector $\\boldsymbol{x}_j$.\n",
|
||
"\n",
|
||
"Replacing $y_i$ with $y_i - y_i - \\overline{\\boldsymbol{y}}$ and centering also our design matrix results in a cost function (in vector-matrix disguise)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7c91d280",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"C(\\boldsymbol{\\beta}) = (\\boldsymbol{\\tilde{y}} - \\tilde{X}\\boldsymbol{\\beta})^T(\\boldsymbol{\\tilde{y}} - \\tilde{X}\\boldsymbol{\\beta}).\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1b81d6de",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"If we minimize with respect to $\\boldsymbol{\\beta}$ we have then"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7d6d497e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\hat{\\boldsymbol{\\beta}} = (\\tilde{X}^T\\tilde{X})^{-1}\\tilde{X}^T\\boldsymbol{\\tilde{y}},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "772593b2",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where $\\boldsymbol{\\tilde{y}} = \\boldsymbol{y} - \\overline{\\boldsymbol{y}}$\n",
|
||
"and $\\tilde{X}_{ij} = X_{ij} - \\frac{1}{n}\\sum_{k=0}^{n-1}X_{kj}$.\n",
|
||
"\n",
|
||
"For Ridge regression we need to add $\\lambda \\boldsymbol{\\beta}^T\\boldsymbol{\\beta}$ to the cost function and get then"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3edbdb56",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\hat{\\boldsymbol{\\beta}} = (\\tilde{X}^T\\tilde{X} + \\lambda I)^{-1}\\tilde{X}^T\\boldsymbol{\\tilde{y}}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7de9723e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"What does this mean? And why do we insist on all this? Let us look at some examples.\n",
|
||
"\n",
|
||
"This code shows a simple first-order fit to a data set using the above transformed data, where we consider the role of the intercept first, by either excluding it or including it (*code example thanks to Øyvind Sigmundson Schøyen*). Here our scaling of the data is done by subtracting the mean values only.\n",
|
||
"Note also that we do not split the data into training and test."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "2e4a0363",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"True beta: [2, 0.5, 3.7]\n",
|
||
"Fitted beta: [2.08376632 0.19569961 3.97898392]\n",
|
||
"Sklearn fitted beta: [2.08376632 0.19569961 3.97898392]\n",
|
||
"MSE with intercept column\n",
|
||
"0.00411363461744314\n",
|
||
"MSE with intercept column from SKL\n",
|
||
"0.004113634617443147\n",
|
||
"Manual intercept: 2.083766322923899\n",
|
||
"Fitted beta (wiothout intercept): [0.19569961 3.97898392]\n",
|
||
"Sklearn intercept: 2.0837663229239043\n",
|
||
"Sklearn fitted beta (without intercept): [0.19569961 3.97898392]\n",
|
||
"MSE with Manual intercept\n",
|
||
"0.00411363461744314\n",
|
||
"MSE with Sklearn intercept\n",
|
||
"0.004113634617443131\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_112_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"from sklearn.linear_model import LinearRegression\n",
|
||
"\n",
|
||
"\n",
|
||
"np.random.seed(2021)\n",
|
||
"\n",
|
||
"def MSE(y_data,y_model):\n",
|
||
" n = np.size(y_model)\n",
|
||
" return np.sum((y_data-y_model)**2)/n\n",
|
||
"\n",
|
||
"\n",
|
||
"def fit_beta(X, y):\n",
|
||
" return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
|
||
"\n",
|
||
"\n",
|
||
"true_beta = [2, 0.5, 3.7]\n",
|
||
"\n",
|
||
"x = np.linspace(0, 1, 11)\n",
|
||
"y = np.sum(\n",
|
||
" np.asarray([x ** p * b for p, b in enumerate(true_beta)]), axis=0\n",
|
||
") + 0.1 * np.random.normal(size=len(x))\n",
|
||
"\n",
|
||
"degree = 3\n",
|
||
"X = np.zeros((len(x), degree))\n",
|
||
"\n",
|
||
"# Include the intercept in the design matrix\n",
|
||
"for p in range(degree):\n",
|
||
" X[:, p] = x ** p\n",
|
||
"\n",
|
||
"beta = fit_beta(X, y)\n",
|
||
"\n",
|
||
"# Intercept is included in the design matrix\n",
|
||
"skl = LinearRegression(fit_intercept=False).fit(X, y)\n",
|
||
"\n",
|
||
"print(f\"True beta: {true_beta}\")\n",
|
||
"print(f\"Fitted beta: {beta}\")\n",
|
||
"print(f\"Sklearn fitted beta: {skl.coef_}\")\n",
|
||
"ypredictOwn = X @ beta\n",
|
||
"ypredictSKL = skl.predict(X)\n",
|
||
"print(f\"MSE with intercept column\")\n",
|
||
"print(MSE(y,ypredictOwn))\n",
|
||
"print(f\"MSE with intercept column from SKL\")\n",
|
||
"print(MSE(y,ypredictSKL))\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.figure()\n",
|
||
"plt.scatter(x, y, label=\"Data\")\n",
|
||
"plt.plot(x, X @ beta, label=\"Fit\")\n",
|
||
"plt.plot(x, skl.predict(X), label=\"Sklearn (fit_intercept=False)\")\n",
|
||
"\n",
|
||
"\n",
|
||
"# Do not include the intercept in the design matrix\n",
|
||
"X = np.zeros((len(x), degree - 1))\n",
|
||
"\n",
|
||
"for p in range(degree - 1):\n",
|
||
" X[:, p] = x ** (p + 1)\n",
|
||
"\n",
|
||
"# Intercept is not included in the design matrix\n",
|
||
"skl = LinearRegression(fit_intercept=True).fit(X, y)\n",
|
||
"\n",
|
||
"# Use centered values for X and y when computing coefficients\n",
|
||
"y_offset = np.average(y, axis=0)\n",
|
||
"X_offset = np.average(X, axis=0)\n",
|
||
"\n",
|
||
"beta = fit_beta(X - X_offset, y - y_offset)\n",
|
||
"intercept = np.mean(y_offset - X_offset @ beta)\n",
|
||
"\n",
|
||
"print(f\"Manual intercept: {intercept}\")\n",
|
||
"print(f\"Fitted beta (wiothout intercept): {beta}\")\n",
|
||
"print(f\"Sklearn intercept: {skl.intercept_}\")\n",
|
||
"print(f\"Sklearn fitted beta (without intercept): {skl.coef_}\")\n",
|
||
"ypredictOwn = X @ beta\n",
|
||
"ypredictSKL = skl.predict(X)\n",
|
||
"print(f\"MSE with Manual intercept\")\n",
|
||
"print(MSE(y,ypredictOwn+intercept))\n",
|
||
"print(f\"MSE with Sklearn intercept\")\n",
|
||
"print(MSE(y,ypredictSKL))\n",
|
||
"\n",
|
||
"plt.plot(x, X @ beta + intercept, \"--\", label=\"Fit (manual intercept)\")\n",
|
||
"plt.plot(x, skl.predict(X), \"--\", label=\"Sklearn (fit_intercept=True)\")\n",
|
||
"plt.grid()\n",
|
||
"plt.legend()\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c6d3a071",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The intercept is the value of our output/target variable\n",
|
||
"when all our features are zero and our function crosses the $y$-axis (for a one-dimensional case). \n",
|
||
"\n",
|
||
"Printing the MSE, we see first that both methods give the same MSE, as\n",
|
||
"they should. However, when we move to for example Ridge regression,\n",
|
||
"the way we treat the intercept may give a larger or smaller MSE,\n",
|
||
"meaning that the MSE can be penalized by the value of the\n",
|
||
"intercept. Not including the intercept in the fit, means that the\n",
|
||
"regularization term does not include $\\beta_0$. For different values\n",
|
||
"of $\\lambda$, this may lead to different MSE values. \n",
|
||
"\n",
|
||
"To remind the reader, the regularization term, with the intercept in Ridge regression, is given by"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "db803b77",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_2^2 = \\lambda \\sum_{j=0}^{p-1}\\beta_j^2,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d7a9c9d8",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"but when we take out the intercept, this equation becomes"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e08af763",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_2^2 = \\lambda \\sum_{j=1}^{p-1}\\beta_j^2.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "10fe24e4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"For Lasso regression we have"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6feb3bdd",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_1 = \\lambda \\sum_{j=1}^{p-1}\\vert\\beta_j\\vert.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "657a0777",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"It means that, when scaling the design matrix and the outputs/targets,\n",
|
||
"by subtracting the mean values, we have an optimization problem which\n",
|
||
"is not penalized by the intercept. The MSE value can then be smaller\n",
|
||
"since it focuses only on the remaining quantities. If we however bring\n",
|
||
"back the intercept, we will get a MSE which then contains the\n",
|
||
"intercept.\n",
|
||
"\n",
|
||
"Armed with this wisdom, we attempt first to simply set the intercept equal to **False** in our implementation of Ridge regression for our well-known vanilla data set."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "9f21317d",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Beta values for own Ridge implementation\n",
|
||
"[ 1.03032441e+00 6.28336218e-02 -6.24175744e-01 5.21169159e-02\n",
|
||
" 2.80847477e-01 2.12552073e-01 8.13220608e-02 -1.69634577e-02\n",
|
||
" -6.50846111e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02\n",
|
||
" -9.80609616e-03 1.08299273e-02 2.41882037e-02 2.93492130e-02\n",
|
||
" 2.64742912e-02 1.63249532e-02 -5.01831050e-05 -2.15098090e-02]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[ 1.03032441e+00 6.28336218e-02 -6.24175744e-01 5.21169159e-02\n",
|
||
" 2.80847477e-01 2.12552073e-01 8.13220608e-02 -1.69634577e-02\n",
|
||
" -6.50846112e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02\n",
|
||
" -9.80609615e-03 1.08299273e-02 2.41882037e-02 2.93492130e-02\n",
|
||
" 2.64742912e-02 1.63249532e-02 -5.01831190e-05 -2.15098090e-02]\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"4.3632959111950474e-07\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"4.363295916366933e-07\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[ 1.03630548 -0.01963611 -0.37900111 -0.07062318 0.12182967 0.16343471\n",
|
||
" 0.13003291 0.07490892 0.02365049 -0.01449782 -0.03814292 -0.04909093\n",
|
||
" -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724 0.01348565\n",
|
||
" 0.02976145 0.04543942]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[ 1.03630548 -0.01963611 -0.37900111 -0.07062318 0.12182967 0.16343471\n",
|
||
" 0.13003291 0.07490892 0.02365049 -0.01449782 -0.03814292 -0.04909093\n",
|
||
" -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724 0.01348565\n",
|
||
" 0.02976145 0.04543942]\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"5.194042826649355e-06\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"5.194042826815211e-06\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[ 1.04220758 -0.10931453 -0.17641709 -0.06020587 0.02208512 0.05789007\n",
|
||
" 0.06491736 0.05785343 0.04537385 0.03196357 0.01969145 0.00934499\n",
|
||
" 0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318\n",
|
||
" -0.01708852 -0.01708781]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[ 1.04220758 -0.10931453 -0.17641709 -0.06020587 0.02208512 0.05789007\n",
|
||
" 0.06491736 0.05785343 0.04537385 0.03196357 0.01969145 0.00934499\n",
|
||
" 0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318\n",
|
||
" -0.01708852 -0.01708781]\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"2.0940821989652176e-05\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"2.0940821989627646e-05\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855 0.00312361\n",
|
||
" 0.01463049 0.01975848 0.02123176 0.02068067 0.01905883 0.01691985\n",
|
||
" 0.01458337 0.01223198 0.00996754 0.00784393 0.00588657 0.00410387\n",
|
||
" 0.00249435 0.00105081]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855 0.00312361\n",
|
||
" 0.01463049 0.01975848 0.02123176 0.02068067 0.01905883 0.01691985\n",
|
||
" 0.01458337 0.01223198 0.00996754 0.00784393 0.00588657 0.00410387\n",
|
||
" 0.00249435 0.00105081]\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"0.00031535148309577417\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"0.0003153514830958095\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[ 8.38916861e-01 1.31276579e-01 8.97497404e-03 -1.72271878e-02\n",
|
||
" -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02\n",
|
||
" -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03\n",
|
||
" -1.84923989e-03 -8.13661243e-04 7.46984697e-06 6.56636616e-04\n",
|
||
" 1.16805821e-03 1.56912044e-03 1.88168312e-03 2.12318726e-03]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[ 8.38916861e-01 1.31276579e-01 8.97497404e-03 -1.72271878e-02\n",
|
||
" -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02\n",
|
||
" -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03\n",
|
||
" -1.84923989e-03 -8.13661243e-04 7.46984697e-06 6.56636616e-04\n",
|
||
" 1.16805821e-03 1.56912044e-03 1.88168312e-03 2.12318726e-03]\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"0.01507238889517717\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"0.01507238889517706\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[0.37396662 0.14174745 0.0764924 0.04892055 0.03447512 0.02586427\n",
|
||
" 0.02024962 0.01633913 0.01347916 0.0113104 0.0096208 0.00827728\n",
|
||
" 0.00719176 0.00630331 0.00556826 0.0049544 0.00443743 0.0039987\n",
|
||
" 0.0036237 0.003301 ]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[0.37396662 0.14174745 0.0764924 0.04892055 0.03447512 0.02586427\n",
|
||
" 0.02024962 0.01633913 0.01347916 0.0113104 0.0096208 0.00827728\n",
|
||
" 0.00719176 0.00630331 0.00556826 0.0049544 0.00443743 0.0039987\n",
|
||
" 0.0036237 0.003301 ]\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"0.2640931530791004\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"0.26409315307910025\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_120_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn import linear_model\n",
|
||
"\n",
|
||
"def MSE(y_data,y_model):\n",
|
||
" n = np.size(y_model)\n",
|
||
" return np.sum((y_data-y_model)**2)/n\n",
|
||
"\n",
|
||
"\n",
|
||
"# A seed just to ensure that the random numbers are the same for every run.\n",
|
||
"# Useful for eventual debugging.\n",
|
||
"np.random.seed(3155)\n",
|
||
"\n",
|
||
"n = 100\n",
|
||
"x = np.random.rand(n)\n",
|
||
"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)\n",
|
||
"\n",
|
||
"Maxpolydegree = 20\n",
|
||
"X = np.zeros((n,Maxpolydegree))\n",
|
||
"#We include explicitely the intercept column\n",
|
||
"for degree in range(Maxpolydegree):\n",
|
||
" X[:,degree] = x**degree\n",
|
||
"# We split the data in test and training data\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
|
||
"\n",
|
||
"p = Maxpolydegree\n",
|
||
"I = np.eye(p,p)\n",
|
||
"# Decide which values of lambda to use\n",
|
||
"nlambdas = 6\n",
|
||
"MSEOwnRidgePredict = np.zeros(nlambdas)\n",
|
||
"MSERidgePredict = np.zeros(nlambdas)\n",
|
||
"lambdas = np.logspace(-4, 2, nlambdas)\n",
|
||
"for i in range(nlambdas):\n",
|
||
" lmb = lambdas[i]\n",
|
||
" OwnRidgeBeta = np.linalg.pinv(X_train.T @ X_train+lmb*I) @ X_train.T @ y_train\n",
|
||
" # Note: we include the intercept column and no scaling\n",
|
||
" RegRidge = linear_model.Ridge(lmb,fit_intercept=False)\n",
|
||
" RegRidge.fit(X_train,y_train)\n",
|
||
" # and then make the prediction\n",
|
||
" ytildeOwnRidge = X_train @ OwnRidgeBeta\n",
|
||
" ypredictOwnRidge = X_test @ OwnRidgeBeta\n",
|
||
" ytildeRidge = RegRidge.predict(X_train)\n",
|
||
" ypredictRidge = RegRidge.predict(X_test)\n",
|
||
" MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)\n",
|
||
" MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
|
||
" print(\"Beta values for own Ridge implementation\")\n",
|
||
" print(OwnRidgeBeta)\n",
|
||
" print(\"Beta values for Scikit-Learn Ridge implementation\")\n",
|
||
" print(RegRidge.coef_)\n",
|
||
" print(\"MSE values for own Ridge implementation\")\n",
|
||
" print(MSEOwnRidgePredict[i])\n",
|
||
" print(\"MSE values for Scikit-Learn Ridge implementation\")\n",
|
||
" print(MSERidgePredict[i])\n",
|
||
"\n",
|
||
"# Now plot the results\n",
|
||
"plt.figure()\n",
|
||
"plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE own Ridge Test')\n",
|
||
"plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test')\n",
|
||
"\n",
|
||
"plt.xlabel('log10(lambda)')\n",
|
||
"plt.ylabel('MSE')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "20de82fd",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The results here agree when we force **Scikit-Learn**'s Ridge function to include the first column in our design matrix.\n",
|
||
"We see that the results agree very well. Here we have thus explicitely included the intercept column in the design matrix.\n",
|
||
"What happens if we do not include the intercept in our fit?\n",
|
||
"Let us see how we can change this code by zero centering."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "4138ed50",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Beta values for own Ridge implementation\n",
|
||
"[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03 2.47116868e-01\n",
|
||
" 2.18613217e-01 1.02054837e-01 -4.25617658e-04 -5.90475506e-02\n",
|
||
" -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02\n",
|
||
" 1.11482289e-02 2.88529063e-02 3.67047975e-02 3.38135733e-02\n",
|
||
" 2.02198703e-02 -3.46383926e-03 -3.63025821e-02]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03 2.47116868e-01\n",
|
||
" 2.18613217e-01 1.02054837e-01 -4.25617655e-04 -5.90475506e-02\n",
|
||
" -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02\n",
|
||
" 1.11482289e-02 2.88529063e-02 3.67047975e-02 3.38135733e-02\n",
|
||
" 2.02198702e-02 -3.46383925e-03 -3.63025821e-02]\n",
|
||
"Intercept from own implementation:\n",
|
||
"1.0330308045187757\n",
|
||
"Intercept from Scikit-Learn Ridge implementation\n",
|
||
"1.0330308045183194\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"3.139255958997547e-06\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"3.1392559585020426e-06\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[-0.05807125 -0.29822833 -0.08551306 0.08156108 0.13679863 0.12333649\n",
|
||
" 0.08251519 0.03815288 0.00111756 -0.02498832 -0.04010697 -0.04566964\n",
|
||
" -0.04355837 -0.03562355 -0.02348765 -0.00848904 0.00831018 0.0260906\n",
|
||
" 0.04423486]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[-0.05807125 -0.29822833 -0.08551306 0.08156108 0.13679863 0.12333649\n",
|
||
" 0.08251519 0.03815288 0.00111756 -0.02498832 -0.04010697 -0.04566964\n",
|
||
" -0.04355837 -0.03562355 -0.02348765 -0.00848904 0.00831018 0.0260906\n",
|
||
" 0.04423486]\n",
|
||
"Intercept from own implementation:\n",
|
||
"1.0411487294305088\n",
|
||
"Intercept from Scikit-Learn Ridge implementation\n",
|
||
"1.0411487294305226\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"1.9601304850035702e-05\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"1.9601304850073734e-05\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[-0.1416398 -0.14021063 -0.05383795 0.01367553 0.04784395 0.05796251\n",
|
||
" 0.05447415 0.044613 0.03267527 0.02098261 0.01066519 0.00217499\n",
|
||
" -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081 -0.01416528\n",
|
||
" -0.01290947]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[-0.1416398 -0.14021063 -0.05383795 0.01367553 0.04784395 0.05796251\n",
|
||
" 0.05447415 0.044613 0.03267527 0.02098261 0.01066519 0.00217499\n",
|
||
" -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081 -0.01416528\n",
|
||
" -0.01290947]\n",
|
||
"Intercept from own implementation:\n",
|
||
"1.049556996627824\n",
|
||
"Intercept from Scikit-Learn Ridge implementation\n",
|
||
"1.0495569966278269\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"5.4959161509357395e-05\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"5.4959161509366685e-05\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[-0.13535942 -0.08593216 -0.03568439 -0.0036367 0.01397146 0.02229529\n",
|
||
" 0.02503753 0.0245528 0.02228115 0.01908936 0.01549377 0.01179792\n",
|
||
" 0.00817631 0.00472512 0.00149311 -0.00149956 -0.00424967 -0.00676387\n",
|
||
" -0.00905423]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[-0.13535942 -0.08593216 -0.03568439 -0.0036367 0.01397146 0.02229529\n",
|
||
" 0.02503753 0.0245528 0.02228115 0.01908936 0.01549377 0.01179792\n",
|
||
" 0.00817631 0.00472512 0.00149311 -0.00149956 -0.00424967 -0.00676387\n",
|
||
" -0.00905423]\n",
|
||
"Intercept from own implementation:\n",
|
||
"1.039967668952797\n",
|
||
"Intercept from Scikit-Learn Ridge implementation\n",
|
||
"1.0399676689527975\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"7.571105947979344e-05\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"7.571105947979412e-05\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706 -0.00517114\n",
|
||
" -0.00174276 0.00068734 0.00243186 0.00369758 0.00462287 0.0053018\n",
|
||
" 0.00579953 0.006162 0.00642221 0.00660427 0.00672607 0.0068011\n",
|
||
" 0.00683964]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706 -0.00517114\n",
|
||
" -0.00174276 0.00068734 0.00243186 0.00369758 0.00462287 0.0053018\n",
|
||
" 0.00579953 0.006162 0.00642221 0.00660427 0.00672607 0.0068011\n",
|
||
" 0.00683964]\n",
|
||
"Intercept from own implementation:\n",
|
||
"0.999955585168597\n",
|
||
"Intercept from Scikit-Learn Ridge implementation\n",
|
||
"0.999955585168597\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"0.0007698473260556343\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"0.0007698473260556325\n",
|
||
"Beta values for own Ridge implementation\n",
|
||
"[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335\n",
|
||
" -0.00323332 -0.00274989 -0.0023548 -0.00202756 -0.00175331 -0.00152117\n",
|
||
" -0.001323 -0.0011526 -0.00100519 -0.00087697 -0.00076495 -0.00066668\n",
|
||
" -0.00058016]\n",
|
||
"Beta values for Scikit-Learn Ridge implementation\n",
|
||
"[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335\n",
|
||
" -0.00323332 -0.00274989 -0.0023548 -0.00202756 -0.00175331 -0.00152117\n",
|
||
" -0.001323 -0.0011526 -0.00100519 -0.00087697 -0.00076495 -0.00066668\n",
|
||
" -0.00058016]\n",
|
||
"Intercept from own implementation:\n",
|
||
"0.9637117593816477\n",
|
||
"Intercept from Scikit-Learn Ridge implementation\n",
|
||
"0.9637117593816477\n",
|
||
"MSE values for own Ridge implementation\n",
|
||
"0.0023813163025848865\n",
|
||
"MSE values for Scikit-Learn Ridge implementation\n",
|
||
"0.002381316302584885\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_122_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn import linear_model\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"\n",
|
||
"def MSE(y_data,y_model):\n",
|
||
" n = np.size(y_model)\n",
|
||
" return np.sum((y_data-y_model)**2)/n\n",
|
||
"# A seed just to ensure that the random numbers are the same for every run.\n",
|
||
"# Useful for eventual debugging.\n",
|
||
"np.random.seed(315)\n",
|
||
"\n",
|
||
"n = 100\n",
|
||
"x = np.random.rand(n)\n",
|
||
"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)\n",
|
||
"\n",
|
||
"Maxpolydegree = 20\n",
|
||
"X = np.zeros((n,Maxpolydegree-1))\n",
|
||
"\n",
|
||
"for degree in range(1,Maxpolydegree): #No intercept column\n",
|
||
" X[:,degree-1] = x**(degree)\n",
|
||
"\n",
|
||
"# We split the data in test and training data\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
|
||
"\n",
|
||
"#For our own implementation, we will need to deal with the intercept by centering the design matrix and the target variable\n",
|
||
"X_train_mean = np.mean(X_train,axis=0)\n",
|
||
"#Center by removing mean from each feature\n",
|
||
"X_train_scaled = X_train - X_train_mean \n",
|
||
"X_test_scaled = X_test - X_train_mean\n",
|
||
"#The model intercept (called y_scaler) is given by the mean of the target variable (IF X is centered)\n",
|
||
"#Remove the intercept from the training data.\n",
|
||
"y_scaler = np.mean(y_train) \n",
|
||
"y_train_scaled = y_train - y_scaler \n",
|
||
"\n",
|
||
"p = Maxpolydegree-1\n",
|
||
"I = np.eye(p,p)\n",
|
||
"# Decide which values of lambda to use\n",
|
||
"nlambdas = 6\n",
|
||
"MSEOwnRidgePredict = np.zeros(nlambdas)\n",
|
||
"MSERidgePredict = np.zeros(nlambdas)\n",
|
||
"\n",
|
||
"lambdas = np.logspace(-4, 2, nlambdas)\n",
|
||
"for i in range(nlambdas):\n",
|
||
" lmb = lambdas[i]\n",
|
||
" OwnRidgeBeta = np.linalg.pinv(X_train_scaled.T @ X_train_scaled+lmb*I) @ X_train_scaled.T @ (y_train_scaled)\n",
|
||
" intercept_ = y_scaler - X_train_mean@OwnRidgeBeta #The intercept can be shifted so the model can predict on uncentered data\n",
|
||
" #Add intercept to prediction\n",
|
||
" ypredictOwnRidge = X_test_scaled @ OwnRidgeBeta + y_scaler \n",
|
||
" RegRidge = linear_model.Ridge(lmb)\n",
|
||
" RegRidge.fit(X_train,y_train)\n",
|
||
" ypredictRidge = RegRidge.predict(X_test)\n",
|
||
" MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)\n",
|
||
" MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
|
||
" print(\"Beta values for own Ridge implementation\")\n",
|
||
" print(OwnRidgeBeta) #Intercept is given by mean of target variable\n",
|
||
" print(\"Beta values for Scikit-Learn Ridge implementation\")\n",
|
||
" print(RegRidge.coef_)\n",
|
||
" print('Intercept from own implementation:')\n",
|
||
" print(intercept_)\n",
|
||
" print('Intercept from Scikit-Learn Ridge implementation')\n",
|
||
" print(RegRidge.intercept_)\n",
|
||
" print(\"MSE values for own Ridge implementation\")\n",
|
||
" print(MSEOwnRidgePredict[i])\n",
|
||
" print(\"MSE values for Scikit-Learn Ridge implementation\")\n",
|
||
" print(MSERidgePredict[i])\n",
|
||
"\n",
|
||
"\n",
|
||
"# Now plot the results\n",
|
||
"plt.figure()\n",
|
||
"plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'b--', label = 'MSE own Ridge Test')\n",
|
||
"plt.plot(np.log10(lambdas), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')\n",
|
||
"plt.xlabel('log10(lambda)')\n",
|
||
"plt.ylabel('MSE')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7e9dfda5",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We see here, when compared to the code which includes explicitely the\n",
|
||
"intercept column, that our MSE value is actually smaller. This is\n",
|
||
"because the regularization term does not include the intercept value\n",
|
||
"$\\beta_0$ in the fitting. This applies to Lasso regularization as\n",
|
||
"well. It means that our optimization is now done only with the\n",
|
||
"centered matrix and/or vector that enter the fitting procedure. Note\n",
|
||
"also that the problem with the intercept occurs mainly in these type\n",
|
||
"of polynomial fitting problem.\n",
|
||
"\n",
|
||
"The next example is indeed an example where all these discussions about the role of intercept are not present."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a7eb252c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## More complicated Example: The Ising model\n",
|
||
"\n",
|
||
"The one-dimensional Ising model with nearest neighbor interaction, no\n",
|
||
"external field and a constant coupling constant $J$ is given by"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "990ff280",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto1\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" H = -J \\sum_{k}^L s_k s_{k + 1},\n",
|
||
"\\label{_auto1} \\tag{1}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5ea46b31",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where $s_i \\in \\{-1, 1\\}$ and $s_{N + 1} = s_1$. The number of spins\n",
|
||
"in the system is determined by $L$. For the one-dimensional system\n",
|
||
"there is no phase transition.\n",
|
||
"\n",
|
||
"We will look at a system of $L = 40$ spins with a coupling constant of\n",
|
||
"$J = 1$. To get enough training data we will generate 10000 states\n",
|
||
"with their respective energies."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "fa60aaab",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
|
||
"import seaborn as sns\n",
|
||
"import scipy.linalg as scl\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"import tqdm\n",
|
||
"sns.set(color_codes=True)\n",
|
||
"cmap_args=dict(vmin=-1., vmax=1., cmap='seismic')\n",
|
||
"\n",
|
||
"L = 40\n",
|
||
"n = int(1e4)\n",
|
||
"\n",
|
||
"spins = np.random.choice([-1, 1], size=(n, L))\n",
|
||
"J = 1.0\n",
|
||
"\n",
|
||
"energies = np.zeros(n)\n",
|
||
"\n",
|
||
"for i in range(n):\n",
|
||
" energies[i] = - J * np.dot(spins[i], np.roll(spins[i], 1))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b9e8214e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Here we use ordinary least squares\n",
|
||
"regression to predict the energy for the nearest neighbor\n",
|
||
"one-dimensional Ising model on a ring, i.e., the endpoints wrap\n",
|
||
"around. We will use linear regression to fit a value for\n",
|
||
"the coupling constant to achieve this.\n",
|
||
"\n",
|
||
"A more general form for the one-dimensional Ising model is"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a578cd63",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto2\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" H = - \\sum_j^L \\sum_k^L s_j s_k J_{jk}.\n",
|
||
"\\label{_auto2} \\tag{2}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d90dcfee",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Here we allow for interactions beyond the nearest neighbors and a state dependent\n",
|
||
"coupling constant. This latter expression can be formulated as\n",
|
||
"a matrix-product"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c4eec69a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto3\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" \\boldsymbol{H} = \\boldsymbol{X} J,\n",
|
||
"\\label{_auto3} \\tag{3}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ae46a11b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where $X_{jk} = s_j s_k$ and $J$ is a matrix which consists of the\n",
|
||
"elements $-J_{jk}$. This form of writing the energy fits perfectly\n",
|
||
"with the form utilized in linear regression, that is"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "87ebb9b8",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto4\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" \\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta} + \\boldsymbol{\\epsilon},\n",
|
||
"\\label{_auto4} \\tag{4}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "856efd3f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We split the data in training and test data as discussed in the previous example"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "5ae2828f",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X = np.zeros((n, L ** 2))\n",
|
||
"for i in range(n):\n",
|
||
" X[i] = np.outer(spins[i], spins[i]).ravel()\n",
|
||
"y = energies\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0d6201d9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"In the ordinary least squares method we choose the cost function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4af380a7",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto5\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" C(\\boldsymbol{X}, \\boldsymbol{\\beta})= \\frac{1}{n}\\left\\{(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})^T(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})\\right\\}.\n",
|
||
"\\label{_auto5} \\tag{5}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8e8eefa3",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We then find the extremal point of $C$ by taking the derivative with respect to $\\boldsymbol{\\beta}$ as discussed above.\n",
|
||
"This yields the expression for $\\boldsymbol{\\beta}$ to be"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a1c0765c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\boldsymbol{\\beta} = \\frac{\\boldsymbol{X}^T \\boldsymbol{y}}{\\boldsymbol{X}^T \\boldsymbol{X}},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c754e28b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which immediately imposes some requirements on $\\boldsymbol{X}$ as there must exist\n",
|
||
"an inverse of $\\boldsymbol{X}^T \\boldsymbol{X}$. If the expression we are modeling contains an\n",
|
||
"intercept, i.e., a constant term, we must make sure that the\n",
|
||
"first column of $\\boldsymbol{X}$ consists of $1$. We do this here"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "ff056cf9",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_train_own = np.concatenate(\n",
|
||
" (np.ones(len(X_train))[:, np.newaxis], X_train),\n",
|
||
" axis=1\n",
|
||
")\n",
|
||
"X_test_own = np.concatenate(\n",
|
||
" (np.ones(len(X_test))[:, np.newaxis], X_test),\n",
|
||
" axis=1\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "524f259e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Doing the inversion directly turns out to be a bad idea since the matrix\n",
|
||
"$\\boldsymbol{X}^T\\boldsymbol{X}$ is singular. An alternative approach is to use the **singular\n",
|
||
"value decomposition**. Using the definition of the Moore-Penrose\n",
|
||
"pseudoinverse we can write the equation for $\\boldsymbol{\\beta}$ as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "52be4553",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\boldsymbol{\\beta} = \\boldsymbol{X}^{+}\\boldsymbol{y},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c37ff09a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where the pseudoinverse of $\\boldsymbol{X}$ is given by"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ea49a105",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\boldsymbol{X}^{+} = \\frac{\\boldsymbol{X}^T}{\\boldsymbol{X}^T\\boldsymbol{X}}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bf31067a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Using singular value decomposition we can decompose the matrix $\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma} \\boldsymbol{V}^T$,\n",
|
||
"where $\\boldsymbol{U}$ and $\\boldsymbol{V}$ are orthogonal(unitary) matrices and $\\boldsymbol{\\Sigma}$ contains the singular values (more details below).\n",
|
||
"where $X^{+} = V\\Sigma^{+} U^T$. This reduces the equation for\n",
|
||
"$\\omega$ to"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5ef2c839",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto6\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" \\boldsymbol{\\beta} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^{+} \\boldsymbol{U}^T \\boldsymbol{y}.\n",
|
||
"\\label{_auto6} \\tag{6}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "96bbf134",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Note that solving this equation by actually doing the pseudoinverse\n",
|
||
"(which is what we will do) is not a good idea as this operation scales\n",
|
||
"as $\\mathcal{O}(n^3)$, where $n$ is the number of elements in a\n",
|
||
"general matrix. Instead, doing $QR$-factorization and solving the\n",
|
||
"linear system as an equation would reduce this down to\n",
|
||
"$\\mathcal{O}(n^2)$ operations."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "5e729b1e",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
|
||
" u, s, v = scl.svd(x)\n",
|
||
" return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "80b923d1",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"beta = ols_svd(X_train_own,y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b4cf2f1a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"When extracting the $J$-matrix we need to make sure that we remove the intercept, as is done here"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "93be2c0d",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"J = beta[1:].reshape(L, L)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ae869089",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"A way of looking at the coefficients in $J$ is to plot the matrices as images."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "8d6d8152",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
|
||
" cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 2000x1400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_154_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plt.figure(figsize=(20, 14))\n",
|
||
"im = plt.imshow(J, **cmap_args)\n",
|
||
"plt.title(\"OLS\", fontsize=18)\n",
|
||
"plt.xticks(fontsize=18)\n",
|
||
"plt.yticks(fontsize=18)\n",
|
||
"cb = fig.colorbar(im)\n",
|
||
"cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8092ac84",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"It is interesting to note that OLS\n",
|
||
"considers both $J_{j, j + 1} = -0.5$ and $J_{j, j - 1} = -0.5$ as\n",
|
||
"valid matrix elements for $J$.\n",
|
||
"In our discussion below on hyperparameters and Ridge and Lasso regression we will see that\n",
|
||
"this problem can be removed, partly and only with Lasso regression. \n",
|
||
"\n",
|
||
"In this case our matrix inversion was actually possible. The obvious question now is what is the mathematics behind the SVD?\n",
|
||
"\n",
|
||
"Let us now \n",
|
||
"focus on Ridge and Lasso regression as well. We repeat some of the\n",
|
||
"basic parts of the Ising model and the setup of the training and test\n",
|
||
"data. The one-dimensional Ising model with nearest neighbor\n",
|
||
"interaction, no external field and a constant coupling constant $J$ is\n",
|
||
"given by"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d9251cde",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto7\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" H = -J \\sum_{k}^L s_k s_{k + 1},\n",
|
||
"\\label{_auto7} \\tag{7}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "abe454c9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where $s_i \\in \\{-1, 1\\}$ and $s_{N + 1} = s_1$. The number of spins in the system is determined by $L$. For the one-dimensional system there is no phase transition.\n",
|
||
"\n",
|
||
"We will look at a system of $L = 40$ spins with a coupling constant of $J = 1$. To get enough training data we will generate 10000 states with their respective energies."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "89f8fbd0",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
|
||
"import seaborn as sns\n",
|
||
"import scipy.linalg as scl\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"import sklearn.linear_model as skl\n",
|
||
"import tqdm\n",
|
||
"sns.set(color_codes=True)\n",
|
||
"cmap_args=dict(vmin=-1., vmax=1., cmap='seismic')\n",
|
||
"\n",
|
||
"L = 40\n",
|
||
"n = int(1e4)\n",
|
||
"\n",
|
||
"spins = np.random.choice([-1, 1], size=(n, L))\n",
|
||
"J = 1.0\n",
|
||
"\n",
|
||
"energies = np.zeros(n)\n",
|
||
"\n",
|
||
"for i in range(n):\n",
|
||
" energies[i] = - J * np.dot(spins[i], np.roll(spins[i], 1))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e06639d4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"A more general form for the one-dimensional Ising model is"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "43ef0f40",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto8\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" H = - \\sum_j^L \\sum_k^L s_j s_k J_{jk}.\n",
|
||
"\\label{_auto8} \\tag{8}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "523dc03b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Here we allow for interactions beyond the nearest neighbors and a more\n",
|
||
"adaptive coupling matrix. This latter expression can be formulated as\n",
|
||
"a matrix-product on the form"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0f389eef",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto9\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" H = X J,\n",
|
||
"\\label{_auto9} \\tag{9}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b6e00f24",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where $X_{jk} = s_j s_k$ and $J$ is the matrix consisting of the\n",
|
||
"elements $-J_{jk}$. This form of writing the energy fits perfectly\n",
|
||
"with the form utilized in linear regression, viz."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5ea1084d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto10\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" \\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta} + \\boldsymbol{\\epsilon}.\n",
|
||
"\\label{_auto10} \\tag{10}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5551bb05",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We organize the data as we did above"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "f5dd7795",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X = np.zeros((n, L ** 2))\n",
|
||
"for i in range(n):\n",
|
||
" X[i] = np.outer(spins[i], spins[i]).ravel()\n",
|
||
"y = energies\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.96)\n",
|
||
"\n",
|
||
"X_train_own = np.concatenate(\n",
|
||
" (np.ones(len(X_train))[:, np.newaxis], X_train),\n",
|
||
" axis=1\n",
|
||
")\n",
|
||
"\n",
|
||
"X_test_own = np.concatenate(\n",
|
||
" (np.ones(len(X_test))[:, np.newaxis], X_test),\n",
|
||
" axis=1\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f36c807b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We will do all fitting with **Scikit-Learn**,"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "eb701dd4",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"clf = skl.LinearRegression().fit(X_train, y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bd356ada",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"When extracting the $J$-matrix we make sure to remove the intercept"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "ae737db8",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"J_sk = clf.coef_.reshape(L, L)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d152a32a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"And then we plot the results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "8d713977",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
|
||
" cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 2000x1400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_172_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plt.figure(figsize=(20, 14))\n",
|
||
"im = plt.imshow(J_sk, **cmap_args)\n",
|
||
"plt.title(\"LinearRegression from Scikit-learn\", fontsize=18)\n",
|
||
"plt.xticks(fontsize=18)\n",
|
||
"plt.yticks(fontsize=18)\n",
|
||
"cb = fig.colorbar(im)\n",
|
||
"cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ede30c18",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The results agree perfectly with our previous discussion where we used our own code.\n",
|
||
"\n",
|
||
"Having explored the ordinary least squares we move on to ridge\n",
|
||
"regression. In ridge regression we include a **regularizer**. This\n",
|
||
"involves a new cost function which leads to a new estimate for the\n",
|
||
"weights $\\boldsymbol{\\beta}$. This results in a penalized regression problem. The\n",
|
||
"cost function is given by"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eac772e9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto11\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" C(\\boldsymbol{X}, \\boldsymbol{\\beta}; \\lambda) = (\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})^T(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y}) + \\lambda \\boldsymbol{\\beta}^T\\boldsymbol{\\beta}.\n",
|
||
"\\label{_auto11} \\tag{11}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "b66675f0",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
|
||
" cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 2000x1400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_175_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"_lambda = 0.1\n",
|
||
"clf_ridge = skl.Ridge(alpha=_lambda).fit(X_train, y_train)\n",
|
||
"J_ridge_sk = clf_ridge.coef_.reshape(L, L)\n",
|
||
"fig = plt.figure(figsize=(20, 14))\n",
|
||
"im = plt.imshow(J_ridge_sk, **cmap_args)\n",
|
||
"plt.title(\"Ridge from Scikit-learn\", fontsize=18)\n",
|
||
"plt.xticks(fontsize=18)\n",
|
||
"plt.yticks(fontsize=18)\n",
|
||
"cb = fig.colorbar(im)\n",
|
||
"cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "71aa37b5",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"In the **Least Absolute Shrinkage and Selection Operator** (LASSO)-method we get a third cost function."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8b70ef66",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto12\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
" C(\\boldsymbol{X}, \\boldsymbol{\\beta}; \\lambda) = (\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})^T(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y}) + \\lambda \\sqrt{\\boldsymbol{\\beta}^T\\boldsymbol{\\beta}}.\n",
|
||
"\\label{_auto12} \\tag{12}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "079981ff",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Finding the extremal point of this cost function is not so straight-forward as in least squares and ridge. We will therefore rely solely on the function ``Lasso`` from **Scikit-Learn**."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "c8d4a180",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
|
||
" cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 2000x1400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_179_1.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"clf_lasso = skl.Lasso(alpha=_lambda).fit(X_train, y_train)\n",
|
||
"J_lasso_sk = clf_lasso.coef_.reshape(L, L)\n",
|
||
"fig = plt.figure(figsize=(20, 14))\n",
|
||
"im = plt.imshow(J_lasso_sk, **cmap_args)\n",
|
||
"plt.title(\"Lasso from Scikit-learn\", fontsize=18)\n",
|
||
"plt.xticks(fontsize=18)\n",
|
||
"plt.yticks(fontsize=18)\n",
|
||
"cb = fig.colorbar(im)\n",
|
||
"cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "57839941",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"It is quite striking how LASSO breaks the symmetry of the coupling\n",
|
||
"constant as opposed to ridge and OLS. We get a sparse solution with\n",
|
||
"$J_{j, j + 1} = -1$.\n",
|
||
"\n",
|
||
"We see how the different models perform for a different set of values for $\\lambda$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "0e3bbb4e",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
" 0%| | 0/10 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00\n",
|
||
" model = cd_fast.enet_coordinate_descent(\n",
|
||
"\r",
|
||
" 10%|███████████████████▏ | 1/10 [00:00<00:06, 1.29it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
" 20%|██████████████████████████████████████▍ | 2/10 [00:01<00:06, 1.18it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
" 30%|█████████████████████████████████████████████████████████▌ | 3/10 [00:02<00:05, 1.29it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
" 40%|████████████████████████████████████████████████████████████████████████████▊ | 4/10 [00:03<00:04, 1.32it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
" 50%|████████████████████████████████████████████████████████████████████████████████████████████████ | 5/10 [00:03<00:03, 1.54it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
" 60%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 6/10 [00:04<00:02, 1.65it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
" 70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 7/10 [00:04<00:02, 1.44it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
" 80%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 8/10 [00:05<00:01, 1.50it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
" 90%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 9/10 [00:06<00:00, 1.47it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.52it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r",
|
||
"100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:06<00:00, 1.45it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 3200x5400 with 30 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_181_13.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"lambdas = np.logspace(-4, 5, 10)\n",
|
||
"\n",
|
||
"train_errors = {\n",
|
||
" \"ols_sk\": np.zeros(lambdas.size),\n",
|
||
" \"ridge_sk\": np.zeros(lambdas.size),\n",
|
||
" \"lasso_sk\": np.zeros(lambdas.size)\n",
|
||
"}\n",
|
||
"\n",
|
||
"test_errors = {\n",
|
||
" \"ols_sk\": np.zeros(lambdas.size),\n",
|
||
" \"ridge_sk\": np.zeros(lambdas.size),\n",
|
||
" \"lasso_sk\": np.zeros(lambdas.size)\n",
|
||
"}\n",
|
||
"\n",
|
||
"plot_counter = 1\n",
|
||
"\n",
|
||
"fig = plt.figure(figsize=(32, 54))\n",
|
||
"\n",
|
||
"for i, _lambda in enumerate(tqdm.tqdm(lambdas)):\n",
|
||
" for key, method in zip(\n",
|
||
" [\"ols_sk\", \"ridge_sk\", \"lasso_sk\"],\n",
|
||
" [skl.LinearRegression(), skl.Ridge(alpha=_lambda), skl.Lasso(alpha=_lambda)]\n",
|
||
" ):\n",
|
||
" method = method.fit(X_train, y_train)\n",
|
||
"\n",
|
||
" train_errors[key][i] = method.score(X_train, y_train)\n",
|
||
" test_errors[key][i] = method.score(X_test, y_test)\n",
|
||
"\n",
|
||
" omega = method.coef_.reshape(L, L)\n",
|
||
"\n",
|
||
" plt.subplot(10, 5, plot_counter)\n",
|
||
" plt.imshow(omega, **cmap_args)\n",
|
||
" plt.title(r\"%s, $\\lambda = %.4f$\" % (key, _lambda))\n",
|
||
" plot_counter += 1\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "14711abd",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We see that LASSO reaches a good solution for low\n",
|
||
"values of $\\lambda$, but will \"wither\" when we increase $\\lambda$ too\n",
|
||
"much. Ridge is more stable over a larger range of values for\n",
|
||
"$\\lambda$, but eventually also fades away.\n",
|
||
"\n",
|
||
"To determine which value of $\\lambda$ is best we plot the accuracy of\n",
|
||
"the models when predicting the training and the testing set. We expect\n",
|
||
"the accuracy of the training set to be quite good, but if the accuracy\n",
|
||
"of the testing set is much lower this tells us that we might be\n",
|
||
"subject to an overfit model. The ideal scenario is an accuracy on the\n",
|
||
"testing set that is close to the accuracy of the training set."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "9ff29a72",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 2000x1400 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"filenames": {
|
||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_183_0.png"
|
||
}
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = plt.figure(figsize=(20, 14))\n",
|
||
"\n",
|
||
"colors = {\n",
|
||
" \"ols_sk\": \"r\",\n",
|
||
" \"ridge_sk\": \"y\",\n",
|
||
" \"lasso_sk\": \"c\"\n",
|
||
"}\n",
|
||
"\n",
|
||
"for key in train_errors:\n",
|
||
" plt.semilogx(\n",
|
||
" lambdas,\n",
|
||
" train_errors[key],\n",
|
||
" colors[key],\n",
|
||
" label=\"Train {0}\".format(key),\n",
|
||
" linewidth=4.0\n",
|
||
" )\n",
|
||
"\n",
|
||
"for key in test_errors:\n",
|
||
" plt.semilogx(\n",
|
||
" lambdas,\n",
|
||
" test_errors[key],\n",
|
||
" colors[key] + \"--\",\n",
|
||
" label=\"Test {0}\".format(key),\n",
|
||
" linewidth=4.0\n",
|
||
" )\n",
|
||
"plt.legend(loc=\"best\", fontsize=18)\n",
|
||
"plt.xlabel(r\"$\\lambda$\", fontsize=18)\n",
|
||
"plt.ylabel(r\"$R^2$\", fontsize=18)\n",
|
||
"plt.tick_params(labelsize=18)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9c10be56",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"From the above figure we can see that LASSO with $\\lambda = 10^{-2}$\n",
|
||
"achieves a very good accuracy on the test set. This by far surpasses the\n",
|
||
"other models for all values of $\\lambda$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "efce3b63",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Exercises and Projects\n",
|
||
"\n",
|
||
"The main aim of this project is to study in more detail various\n",
|
||
"regression methods, including the Ordinary Least Squares (OLS) method,\n",
|
||
"The total score is **100** points. Each subtask has its own final score.\n",
|
||
"\n",
|
||
"We will first study how to fit polynomials to a specific\n",
|
||
"two-dimensional function called [Franke's\n",
|
||
"function](http://www.dtic.mil/dtic/tr/fulltext/u2/a081688.pdf). This\n",
|
||
"is a function which has been widely used when testing various\n",
|
||
"interpolation and fitting algorithms. Furthermore, after having\n",
|
||
"established the model and the method, we will employ resamling\n",
|
||
"techniques such as cross-validation and/or bootstrap in order to perform a\n",
|
||
"proper assessment of our models. We will also study in detail the\n",
|
||
"so-called Bias-Variance trade off.\n",
|
||
"\n",
|
||
"The Franke function, which is a weighted sum of four exponentials reads as follows"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ef5a4df0",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\begin{align*}\n",
|
||
"f(x,y) &= \\frac{3}{4}\\exp{\\left(-\\frac{(9x-2)^2}{4} - \\frac{(9y-2)^2}{4}\\right)}+\\frac{3}{4}\\exp{\\left(-\\frac{(9x+1)^2}{49}- \\frac{(9y+1)}{10}\\right)} \\\\\n",
|
||
"&+\\frac{1}{2}\\exp{\\left(-\\frac{(9x-7)^2}{4} - \\frac{(9y-3)^2}{4}\\right)} -\\frac{1}{5}\\exp{\\left(-(9x-4)^2 - (9y-7)^2\\right) }.\n",
|
||
"\\end{align*}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b085eff4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The function will be defined for $x,y\\in [0,1]$. Our first step will\n",
|
||
"be to perform an OLS regression analysis of this function, trying out\n",
|
||
"a polynomial fit with an $x$ and $y$ dependence of the form $[x, y,\n",
|
||
"x^2, y^2, xy, \\dots]$. We will also include bootstrap first as\n",
|
||
"a resampling technique. After that we will include the cross-validation technique. As in homeworks 1 and 2, we can use a uniform\n",
|
||
"distribution to set up the arrays of values for $x$ and $y$, or as in\n",
|
||
"the example below just a set of fixed \n",
|
||
"values for $x$ and $y$ with a given step\n",
|
||
"size. We will fit a\n",
|
||
"function (for example a polynomial) of $x$ and $y$. Thereafter we\n",
|
||
"will repeat much of the same procedure using the Ridge and Lasso\n",
|
||
"regression methods, introducing thus a dependence on the bias\n",
|
||
"(penalty) $\\lambda$.\n",
|
||
"\n",
|
||
"Finally we are going to use (real) digital terrain data and try to\n",
|
||
"reproduce these data using the same methods. We will also try to go\n",
|
||
"beyond the second-order polynomials metioned above and explore \n",
|
||
"which polynomial fits the data best.\n",
|
||
"\n",
|
||
"The Python code for the Franke function is included here (it performs also a three-dimensional plot of it)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "5056dccb",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"ename": "TypeError",
|
||
"evalue": "gca() got an unexpected keyword argument 'projection'",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
||
"Cell \u001b[0;32mIn[30], line 9\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrandom\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m random, seed\n\u001b[1;32m 8\u001b[0m fig \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[0;32m----> 9\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprojection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m3d\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Make data.\u001b[39;00m\n\u001b[1;32m 12\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0.05\u001b[39m)\n",
|
||
"\u001b[0;31mTypeError\u001b[0m: gca() got an unexpected keyword argument 'projection'"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 0 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from matplotlib import cm\n",
|
||
"from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
|
||
"import numpy as np\n",
|
||
"from random import random, seed\n",
|
||
"\n",
|
||
"fig = plt.figure()\n",
|
||
"ax = fig.gca(projection='3d')\n",
|
||
"\n",
|
||
"# Make data.\n",
|
||
"x = np.arange(0, 1, 0.05)\n",
|
||
"y = np.arange(0, 1, 0.05)\n",
|
||
"x, y = np.meshgrid(x,y)\n",
|
||
"\n",
|
||
"\n",
|
||
"def FrankeFunction(x,y):\n",
|
||
" term1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\n",
|
||
" term2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\n",
|
||
" term3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n",
|
||
" term4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n",
|
||
" return term1 + term2 + term3 + term4\n",
|
||
"\n",
|
||
"\n",
|
||
"z = FrankeFunction(x, y)\n",
|
||
"\n",
|
||
"# Plot the surface.\n",
|
||
"surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm,\n",
|
||
" linewidth=0, antialiased=False)\n",
|
||
"\n",
|
||
"# Customize the z axis.\n",
|
||
"ax.set_zlim(-0.10, 1.40)\n",
|
||
"ax.zaxis.set_major_locator(LinearLocator(10))\n",
|
||
"ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
|
||
"\n",
|
||
"# Add a color bar which maps values to colors.\n",
|
||
"fig.colorbar(surf, shrink=0.5, aspect=5)\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f5e72aef",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"### Exercise: Ordinary Least Square (OLS) on the Franke function\n",
|
||
"\n",
|
||
"We will generate our own dataset for a function\n",
|
||
"$\\mathrm{FrankeFunction}(x,y)$ with $x,y \\in [0,1]$. The function\n",
|
||
"$f(x,y)$ is the Franke function. You should explore also the addition\n",
|
||
"of an added stochastic noise to this function using the normal\n",
|
||
"distribution $N(0,1)$.\n",
|
||
"\n",
|
||
"*Write your own code* (using either a matrix inversion or a singular\n",
|
||
"value decomposition from e.g., **numpy** ) or use your code from\n",
|
||
"homeworks 1 and 2 and perform a standard least square regression\n",
|
||
"analysis using polynomials in $x$ and $y$ up to fifth order. Find the\n",
|
||
"[confidence intervals](https://en.wikipedia.org/wiki/Confidence_interval) of the parameters (estimators) $\\beta$ by computing their\n",
|
||
"variances, evaluate the Mean Squared error (MSE)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0ba33237",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"MSE(\\hat{y},\\hat{\\tilde{y}}) = \\frac{1}{n}\n",
|
||
"\\sum_{i=0}^{n-1}(y_i-\\tilde{y}_i)^2,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1f9af2ef",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"and the $R^2$ score function. If $\\tilde{\\hat{y}}_i$ is the predicted\n",
|
||
"value of the $i-th$ sample and $y_i$ is the corresponding true value,\n",
|
||
"then the score $R^2$ is defined as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "77d5b0f2",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"R^2(\\hat{y}, \\tilde{\\hat{y}}) = 1 - \\frac{\\sum_{i=0}^{n - 1} (y_i - \\tilde{y}_i)^2}{\\sum_{i=0}^{n - 1} (y_i - \\bar{y})^2},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c1f9df9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where we have defined the mean value of $\\hat{y}$ as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1d06102c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\bar{y} = \\frac{1}{n} \\sum_{i=0}^{n - 1} y_i.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8fb40b08",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Your code has to include a scaling of the data (for example by\n",
|
||
"subtracting the mean value), and\n",
|
||
"a split of the data in training and test data. For this exercise you can\n",
|
||
"either write your own code or use for example the function for\n",
|
||
"splitting training data provided by the library **Scikit-Learn** (make\n",
|
||
"sure you have installed it). This function is called\n",
|
||
"$train\\_test\\_split$. **You should present a critical discussion of why and how you have scaled or not scaled the data**.\n",
|
||
"\n",
|
||
"It is normal in essentially all Machine Learning studies to split the\n",
|
||
"data in a training set and a test set (eventually also an additional\n",
|
||
"validation set). There\n",
|
||
"is no explicit recipe for how much data should be included as training\n",
|
||
"data and say test data. An accepted rule of thumb is to use\n",
|
||
"approximately $2/3$ to $4/5$ of the data as training data.\n",
|
||
"\n",
|
||
"You can easily reuse the solutions to your exercises from week 35 and week 36."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1701de47",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"### Exercise: Bias-variance trade-off and resampling techniques\n",
|
||
"\n",
|
||
"Our aim here is to study the bias-variance trade-off by implementing the **bootstrap** resampling technique.\n",
|
||
"\n",
|
||
"With a code which does OLS and includes resampling techniques, \n",
|
||
"we will now discuss the bias-variance trade-off in the context of\n",
|
||
"continuous predictions such as regression. However, many of the\n",
|
||
"intuitions and ideas discussed here also carry over to classification\n",
|
||
"tasks and basically all Machine Learning algorithms. \n",
|
||
"\n",
|
||
"Before you perform an analysis of the bias-variance trade-off on your test data, make\n",
|
||
"first a figure similar to Fig. 2.11 of Hastie, Tibshirani, and\n",
|
||
"Friedman. Figure 2.11 of this reference displays only the test and training MSEs. The test MSE can be used to \n",
|
||
"indicate possible regions of low/high bias and variance. You will most likely not get an\n",
|
||
"equally smooth curve!\n",
|
||
"\n",
|
||
"With this result we move on to the bias-variance trade-off analysis.\n",
|
||
"\n",
|
||
"Consider a\n",
|
||
"dataset $\\mathcal{L}$ consisting of the data\n",
|
||
"$\\mathbf{X}_\\mathcal{L}=\\{(y_j, \\boldsymbol{x}_j), j=0\\ldots n-1\\}$.\n",
|
||
"\n",
|
||
"Let us assume that the true data is generated from a noisy model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "15110cdf",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\boldsymbol{y}=f(\\boldsymbol{x}) + \\boldsymbol{\\epsilon}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "33046595",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Here $\\epsilon$ is normally distributed with mean zero and standard\n",
|
||
"deviation $\\sigma^2$.\n",
|
||
"\n",
|
||
"In our derivation of the ordinary least squares method we defined then\n",
|
||
"an approximation to the function $f$ in terms of the parameters\n",
|
||
"$\\boldsymbol{\\beta}$ and the design matrix $\\boldsymbol{X}$ which embody our model,\n",
|
||
"that is $\\boldsymbol{\\tilde{y}}=\\boldsymbol{X}\\boldsymbol{\\beta}$.\n",
|
||
"\n",
|
||
"The parameters $\\boldsymbol{\\beta}$ are in turn found by optimizing the means\n",
|
||
"squared error via the so-called cost function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "84527747",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"C(\\boldsymbol{X},\\boldsymbol{\\beta}) =\\frac{1}{n}\\sum_{i=0}^{n-1}(y_i-\\tilde{y}_i)^2=\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right].\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "da2f876d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Here the expected value $\\mathbb{E}$ is the sample value. \n",
|
||
"\n",
|
||
"Show that you can rewrite this as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1845a7dc",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\frac{1}{n}\\sum_i(f_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\frac{1}{n}\\sum_i(\\tilde{y}_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\sigma^2.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d2f1d7d7",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Explain what the terms mean, which one is the bias and which one is\n",
|
||
"the variance and discuss their interpretations.\n",
|
||
"\n",
|
||
"Perform then a bias-variance analysis of the Franke function by\n",
|
||
"studying the MSE value as function of the complexity of your model.\n",
|
||
"\n",
|
||
"Discuss the bias and variance trade-off as function\n",
|
||
"of your model complexity (the degree of the polynomial) and the number\n",
|
||
"of data points, and possibly also your training and test data using the **bootstrap** resampling method.\n",
|
||
"\n",
|
||
"Note also that when you calculate the bias, in all applications you don't know the function values $f_i$. You would hence replace them with the actual data points $y_i$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e898c902",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"### Exercise: Cross-validation as resampling techniques, adding more complexity\n",
|
||
"\n",
|
||
"The aim here is to write your own code for another widely popular\n",
|
||
"resampling technique, the so-called cross-validation method. Again,\n",
|
||
"before you start with cross-validation approach, you should scale your\n",
|
||
"data.\n",
|
||
"\n",
|
||
"Implement the $k$-fold cross-validation algorithm (write your own\n",
|
||
"code) and evaluate again the MSE function resulting\n",
|
||
"from the test folds. You can compare your own code with that from\n",
|
||
"**Scikit-Learn** if needed. \n",
|
||
"\n",
|
||
"Compare the MSE you get from your cross-validation code with the one\n",
|
||
"you got from your **bootstrap** code. Comment your results. Try $5-10$\n",
|
||
"folds. You can also compare your own cross-validation code with the\n",
|
||
"one provided by **Scikit-Learn**."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d23dc734",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"### Exercise: Ridge Regression on the Franke function with resampling\n",
|
||
"\n",
|
||
"Write your own code for the Ridge method, either using matrix\n",
|
||
"inversion or the singular value decomposition as done in the previous\n",
|
||
"exercise. Perform the same bootstrap analysis as in the\n",
|
||
"Exercise 2 (for the same polynomials) and the cross-validation in exercise 3 but now for different values of $\\lambda$. Compare and\n",
|
||
"analyze your results with those obtained in exercises 1-3. Study the\n",
|
||
"dependence on $\\lambda$.\n",
|
||
"\n",
|
||
"Study also the bias-variance trade-off as function of various values of\n",
|
||
"the parameter $\\lambda$. For the bias-variance trade-off, use the **bootstrap** resampling method. Comment your results."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "39a35330",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"### Exercise: Lasso Regression on the Franke function with resampling\n",
|
||
"\n",
|
||
"This exercise is essentially a repeat of the previous two ones, but now\n",
|
||
"with Lasso regression. Write either your own code (difficult and optional) or, in this case,\n",
|
||
"you can also use the functionalities of **Scikit-Learn** (recommended). \n",
|
||
"Give a\n",
|
||
"critical discussion of the three methods and a judgement of which\n",
|
||
"model fits the data best. Perform here as well an analysis of the bias-variance trade-off using the **bootstrap** resampling technique and an analysis of the mean squared error using cross-validation."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "44d9e821",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"### Exercise: Analysis of real data\n",
|
||
"\n",
|
||
"With our codes functioning and having been tested properly on a\n",
|
||
"simpler function we are now ready to look at real data. We will\n",
|
||
"essentially repeat in this exercise what was done in exercises 1-5. However, we\n",
|
||
"need first to download the data and prepare properly the inputs to our\n",
|
||
"codes. We are going to download digital terrain data from the website\n",
|
||
"<https://earthexplorer.usgs.gov/>,\n",
|
||
"\n",
|
||
"Or, if you prefer, we have placed selected datafiles at <https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles>\n",
|
||
"\n",
|
||
"In order to obtain data for a specific region, you need to register as\n",
|
||
"a user (free) at this website and then decide upon which area you want\n",
|
||
"to fetch the digital terrain data from. In order to be able to read\n",
|
||
"the data properly, you need to specify that the format should be **SRTM\n",
|
||
"Arc-Second Global** and download the data as a **GeoTIF** file. The\n",
|
||
"files are then stored in *tif* format which can be imported into a\n",
|
||
"Python program using"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "0a4e6d7e",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"scipy.misc.imread"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "16a73292",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Here is a simple part of a Python code which reads and plots the data\n",
|
||
"from such files"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "168356a4",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"import numpy as np\n",
|
||
"from imageio import imread\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||
"from matplotlib import cm\n",
|
||
"\n",
|
||
"# Load the terrain\n",
|
||
"terrain1 = imread('SRTM_data_Norway_1.tif')\n",
|
||
"# Show the terrain\n",
|
||
"plt.figure()\n",
|
||
"plt.title('Terrain over Norway 1')\n",
|
||
"plt.imshow(terrain1, cmap='gray')\n",
|
||
"plt.xlabel('X')\n",
|
||
"plt.ylabel('Y')\n",
|
||
"plt.show()\n",
|
||
"\"\"\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e9ec3e68",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"If you should have problems in downloading the digital terrain data,\n",
|
||
"we provide two examples under the data folder of project 1. One is\n",
|
||
"from a region close to Stavanger in Norway and the other Møsvatn\n",
|
||
"Austfjell, again in Norway.\n",
|
||
"Feel free to produce your own terrain data.\n",
|
||
"\n",
|
||
"Alternatively, if you would like to use another data set, feel free to do so. This could be data close to your reseach area or simply a data set you found interesting. See for example [kaggle.com](https://www.kaggle.com/datasets) for examples.\n",
|
||
"\n",
|
||
"Our final part deals with the parameterization of your digital terrain\n",
|
||
"data (or your own data). We will apply all three methods for linear regression, the same type (or higher order) of polynomial\n",
|
||
"approximation and cross-validation as resampling technique to evaluate which\n",
|
||
"model fits the data best.\n",
|
||
"\n",
|
||
"At the end, you should present a critical evaluation of your results\n",
|
||
"and discuss the applicability of these regression methods to the type\n",
|
||
"of data presented here (either the terrain data we propose or other data sets)."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.18"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
} |