Morten Hjorth-Jensen
3108 lines
767 KiB
Plaintext
3108 lines
767 KiB
Plaintext
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"<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
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"doconce format html week48.do.txt --no_mako -->\n",
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"<!-- dom:TITLE: Week 48: Gradient boosting and summary of course -->"
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"# Week 48: Gradient boosting and summary of course\n",
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"**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway\n",
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"\n",
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"Date: **Nov 25, 2024**\n",
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"\n",
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"Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license"
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"source": [
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"## Overview of week 48"
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"## Lecture Monday, November 25\n",
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"**Plans for the lecture Monday 25 November, with video suggestions etc.**\n",
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"\n",
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"1. Boosting and gradient boosting and ensemble models\n",
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"\n",
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"2. Summary of course\n",
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"\n",
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"3. Readings and Videos:\n",
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"\n",
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"a. These lecture notes at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week48/ipynb/week48.ipynb>\n",
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"\n",
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"b. See also lecture notes from week 47 at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/pub/week47/ipynb/week47.ipynb>. The lecture on Monday starts with a repetition on AdaBoost before we move over to gradient boosting with examples\n",
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"\n",
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"c. Video of lecture at <https://youtu.be/iTaRdAPQnDA>\n",
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"\n",
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"d. Whiteboard notes at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesNovember25.pdf>\n",
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"\n",
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"e. Video on Decision trees <https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn>\n",
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"\n",
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"f. Video on boosting methods <https://www.youtube.com/watch?v=wPqtzj5VZus&ab_channel=H2O.ai>\n",
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"\n",
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"g. Video on AdaBoost <https://www.youtube.com/watch?v=LsK-xG1cLYA>\n",
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"\n",
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"h. Video on Gradient boost, part 1, parts 2-4 follow thereafter <https://www.youtube.com/watch?v=3CC4N4z3GJc>\n",
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"\n",
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"i. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at <https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf>."
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"## Lab sessions\n",
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"**Lab sessions on Tuesday and Wednesday.**\n",
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"\n",
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" * Work and Discussion of project 3\n",
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"\n",
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" * Last weekly exercise\n",
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"\n",
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" * Lab sessions at usual times.\n",
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"\n",
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" * For the week of December 2-6, lab sessions start at 10am and end at 4pm, room FØ434, Tuesday and Wednesday"
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"## Random Forest Algorithm, reminder from last week\n",
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"\n",
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"The algorithm described here can be applied to both classification and regression problems.\n",
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"\n",
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"We will grow of forest of say $B$ trees.\n",
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"* For $b=1:B$\n",
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"\n",
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"a. Draw a bootstrap sample from the training data organized in our $\\boldsymbol{X}$ matrix.\n",
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"\n",
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"b. We grow then a random forest tree $T_b$ based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached\n",
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"\n",
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"1. we select $m \\le p$ variables at random from the $p$ predictors/features\n",
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"\n",
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"2. pick the best split point among the $m$ features using for example the CART algorithm and create a new node\n",
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"\n",
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"3. split the node into daughter nodes\n",
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"\n",
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"Finally we output then the ensemble of trees $\\{T_b\\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem."
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"source": [
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"## Random Forests Compared with other Methods on the Cancer Data"
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"(426, 30)\n",
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"(143, 30)\n",
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"Test set accuracy Logistic Regression with scaled data: 0.96\n",
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"Test set accuracy SVM with scaled data: 0.96\n",
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"Test set accuracy with Decision Trees and scaled data: 0.92\n"
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"[1. 0.73333333 0.93333333 1. 1. 0.92857143\n",
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" 1. 0.92857143 0.92857143 0.92857143]\n",
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"Test set accuracy with Random Forests and scaled data: 0.97\n"
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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"%matplotlib inline\n",
|
||
"\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.model_selection import train_test_split \n",
|
||
"from sklearn.datasets import load_breast_cancer\n",
|
||
"from sklearn.svm import SVC\n",
|
||
"from sklearn.linear_model import LogisticRegression\n",
|
||
"from sklearn.tree import DecisionTreeClassifier\n",
|
||
"from sklearn.ensemble import BaggingClassifier\n",
|
||
"\n",
|
||
"# Load the data\n",
|
||
"cancer = load_breast_cancer()\n",
|
||
"\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
|
||
"print(X_train.shape)\n",
|
||
"print(X_test.shape)\n",
|
||
"#define methods\n",
|
||
"# Logistic Regression\n",
|
||
"logreg = LogisticRegression(solver='lbfgs')\n",
|
||
"# Support vector machine\n",
|
||
"svm = SVC(gamma='auto', C=100)\n",
|
||
"# Decision Trees\n",
|
||
"deep_tree_clf = DecisionTreeClassifier(max_depth=None)\n",
|
||
"#Scale the data\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"scaler = StandardScaler()\n",
|
||
"scaler.fit(X_train)\n",
|
||
"X_train_scaled = scaler.transform(X_train)\n",
|
||
"X_test_scaled = scaler.transform(X_test)\n",
|
||
"# Logistic Regression\n",
|
||
"logreg.fit(X_train_scaled, y_train)\n",
|
||
"print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
|
||
"# Support Vector Machine\n",
|
||
"svm.fit(X_train_scaled, y_train)\n",
|
||
"print(\"Test set accuracy SVM with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
|
||
"# Decision Trees\n",
|
||
"deep_tree_clf.fit(X_train_scaled, y_train)\n",
|
||
"print(\"Test set accuracy with Decision Trees and scaled data: {:.2f}\".format(deep_tree_clf.score(X_test_scaled,y_test)))\n",
|
||
"\n",
|
||
"\n",
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"from sklearn.preprocessing import LabelEncoder\n",
|
||
"from sklearn.model_selection import cross_validate\n",
|
||
"# Data set not specificied\n",
|
||
"#Instantiate the model with 500 trees and entropy as splitting criteria\n",
|
||
"Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion=\"entropy\")\n",
|
||
"Random_Forest_model.fit(X_train_scaled, y_train)\n",
|
||
"#Cross validation\n",
|
||
"accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']\n",
|
||
"print(accuracy)\n",
|
||
"print(\"Test set accuracy with Random Forests and scaled data: {:.2f}\".format(Random_Forest_model.score(X_test_scaled,y_test)))\n",
|
||
"\n",
|
||
"\n",
|
||
"import scikitplot as skplt\n",
|
||
"y_pred = Random_Forest_model.predict(X_test_scaled)\n",
|
||
"skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
|
||
"plt.show()\n",
|
||
"y_probas = Random_Forest_model.predict_proba(X_test_scaled)\n",
|
||
"skplt.metrics.plot_roc(y_test, y_probas)\n",
|
||
"plt.show()\n",
|
||
"skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2bf79506",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Recall that the cumulative gains curve shows the percentage of the\n",
|
||
"overall number of cases in a given category *gained* by targeting a\n",
|
||
"percentage of the total number of cases.\n",
|
||
"\n",
|
||
"Similarly, the receiver operating characteristic curve, or ROC curve,\n",
|
||
"displays the diagnostic ability of a binary classifier system as its\n",
|
||
"discrimination threshold is varied. It plots the true positive rate against the false positive rate."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a80dc181",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Compare Bagging on Trees with Random Forests"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "61ef464c",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"bag_clf = BaggingClassifier(\n",
|
||
" DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n",
|
||
" n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "ab3892d7",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.9790209790209791"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"bag_clf.fit(X_train, y_train)\n",
|
||
"y_pred = bag_clf.predict(X_test)\n",
|
||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||
"rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n",
|
||
"rnd_clf.fit(X_train, y_train)\n",
|
||
"y_pred_rf = rnd_clf.predict(X_test)\n",
|
||
"np.sum(y_pred == y_pred_rf) / len(y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2d4ac770",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Boosting, a Bird's Eye View\n",
|
||
"\n",
|
||
"The basic idea is to combine weak classifiers in order to create a good\n",
|
||
"classifier. With a weak classifier we often intend a classifier which\n",
|
||
"produces results which are only slightly better than we would get by\n",
|
||
"random guesses.\n",
|
||
"\n",
|
||
"This is done by applying in an iterative way a weak (or a standard\n",
|
||
"classifier like decision trees) to modify the data. In each iteration\n",
|
||
"we emphasize those observations which are misclassified by weighting\n",
|
||
"them with a factor."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "20f3e403",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## What is boosting? Additive Modelling/Iterative Fitting\n",
|
||
"\n",
|
||
"Boosting is a way of fitting an additive expansion in a set of\n",
|
||
"elementary basis functions like for example some simple polynomials.\n",
|
||
"Assume for example that we have a function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ea52430d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "86cb9f50",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where $\\beta_m$ are the expansion parameters to be determined in a\n",
|
||
"minimization process and $b(x;\\gamma_m)$ are some simple functions of\n",
|
||
"the multivariable parameter $x$ which is characterized by the\n",
|
||
"parameters $\\gamma_m$.\n",
|
||
"\n",
|
||
"As an example, consider the Sigmoid function we used in logistic\n",
|
||
"regression. In that case, we can translate the function\n",
|
||
"$b(x;\\gamma_m)$ into the Sigmoid function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "00ca4f95",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\sigma(t) = \\frac{1}{1+\\exp{(-t)}},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ddc56700",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where $t=\\gamma_0+\\gamma_1 x$ and the parameters $\\gamma_0$ and\n",
|
||
"$\\gamma_1$ were determined by the Logistic Regression fitting\n",
|
||
"algorithm.\n",
|
||
"\n",
|
||
"As another example, consider the cost function we defined for linear regression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eb17dae2",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9ae66e77",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"In this case the function $f(x)$ was replaced by the design matrix\n",
|
||
"$\\boldsymbol{X}$ and the unknown linear regression parameters $\\boldsymbol{\\beta}$,\n",
|
||
"that is $\\boldsymbol{f}=\\boldsymbol{X}\\boldsymbol{\\beta}$. In linear regression we can \n",
|
||
"simply invert a matrix and obtain the parameters $\\beta$ by"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ae777b9c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\boldsymbol{\\beta}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8b3174c5",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters $\\beta_m$ and $\\gamma_m$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b4590742",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Iterative Fitting, Regression and Squared-error Cost Function\n",
|
||
"\n",
|
||
"The way we proceed is as follows (here we specialize to the squared-error cost function)\n",
|
||
"\n",
|
||
"1. Establish a cost function, here ${\\cal C}(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f_M(x_i))^2$ with $f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m)$.\n",
|
||
"\n",
|
||
"2. Initialize with a guess $f_0(x)$. It could be one or even zero or some random numbers.\n",
|
||
"\n",
|
||
"3. For $m=1:M$\n",
|
||
"\n",
|
||
"a. minimize $\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2$ wrt $\\gamma$ and $\\beta$\n",
|
||
"\n",
|
||
"b. This gives the optimal values $\\beta_m$ and $\\gamma_m$\n",
|
||
"\n",
|
||
"c. Determine then the new values $f_m(x)=f_{m-1}(x) +\\beta_m b(x;\\gamma_m)$\n",
|
||
"\n",
|
||
"We could use any of the algorithms we have discussed till now. If we\n",
|
||
"use trees, $\\gamma$ parameterizes the split variables and split points\n",
|
||
"at the internal nodes, and the predictions at the terminal nodes."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d6f866fd",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Squared-Error Example and Iterative Fitting\n",
|
||
"\n",
|
||
"To better understand what happens, let us develop the steps for the iterative fitting using the above squared error function.\n",
|
||
"\n",
|
||
"For simplicity we assume also that our functions $b(x;\\gamma)=1+\\gamma x$. \n",
|
||
"\n",
|
||
"This means that for every iteration $m$, we need to optimize"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b190e045",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"(\\beta_m,\\gamma_m) = \\mathrm{argmin}_{\\beta,\\lambda}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2=\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta(1+\\gamma x_i))^2.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "df876b5f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We start our iteration by simply setting $f_0(x)=0$. \n",
|
||
"Taking the derivatives with respect to $\\beta$ and $\\gamma$ we obtain"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "33756540",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\frac{\\partial {\\cal C}}{\\partial \\beta} = -2\\sum_{i}(1+\\gamma x_i)(y_i-\\beta(1+\\gamma x_i))=0,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9b588d4a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"and"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5f289c53",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\frac{\\partial {\\cal C}}{\\partial \\gamma} =-2\\sum_{i}\\beta x_i(y_i-\\beta(1+\\gamma x_i))=0.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "23f5d161",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We can then rewrite these equations as (defining $\\boldsymbol{w}=\\boldsymbol{e}+\\gamma \\boldsymbol{x})$ with $\\boldsymbol{e}$ being the unit vector)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fd058392",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\gamma \\boldsymbol{w}^T(\\boldsymbol{y}-\\beta\\gamma \\boldsymbol{w})=0,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5a8517bd",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which gives us $\\beta = \\boldsymbol{w}^T\\boldsymbol{y}/(\\boldsymbol{w}^T\\boldsymbol{w})$. Similarly we have"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "94eec67e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\beta\\gamma \\boldsymbol{x}^T(\\boldsymbol{y}-\\beta(1+\\gamma \\boldsymbol{x}))=0,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d4936ab1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which leads to $\\gamma =(\\boldsymbol{x}^T\\boldsymbol{y}-\\beta\\boldsymbol{x}^T\\boldsymbol{e})/(\\beta\\boldsymbol{x}^T\\boldsymbol{x})$. Inserting\n",
|
||
"for $\\beta$ gives us an equation for $\\gamma$. This is a non-linear equation in the unknown $\\gamma$ and has to be solved numerically. \n",
|
||
"\n",
|
||
"The solution to these two equations gives us in turn $\\beta_1$ and $\\gamma_1$ leading to the new expression for $f_1(x)$ as\n",
|
||
"$f_1(x) = \\beta_1(1+\\gamma_1x)$. Doing this $M$ times results in our final estimate for the function $f$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7bafaad4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Iterative Fitting, Classification and AdaBoost\n",
|
||
"\n",
|
||
"Let us consider a binary classification problem with two outcomes $y_i \\in \\{-1,1\\}$ and $i=0,1,2,\\dots,n-1$ as our set of\n",
|
||
"observations. We define a classification function $G(x)$ which produces a prediction taking one or the other of the two values \n",
|
||
"$\\{-1,1\\}$.\n",
|
||
"\n",
|
||
"The error rate of the training sample is then"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b91cc27d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathrm{\\overline{err}}=\\frac{1}{n} \\sum_{i=0}^{n-1} I(y_i\\ne G(x_i)).\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ee926ea4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The iterative procedure starts with defining a weak classifier whose\n",
|
||
"error rate is barely better than random guessing. The iterative\n",
|
||
"procedure in boosting is to sequentially apply a weak\n",
|
||
"classification algorithm to repeatedly modified versions of the data\n",
|
||
"producing a sequence of weak classifiers $G_m(x)$.\n",
|
||
"\n",
|
||
"Here we will express our function $f(x)$ in terms of $G(x)$. That is"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "88be0c12",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a16704d1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"will be a function of"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1667ae43",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"G_M(x) = \\mathrm{sign} \\sum_{i=1}^M \\alpha_m G_m(x).\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "21368026",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Adaptive Boosting, AdaBoost\n",
|
||
"\n",
|
||
"In our iterative procedure we define thus"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3f57f184",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"f_m(x) = f_{m-1}(x)+\\beta_mG_m(x).\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "adc38409",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the\n",
|
||
"exponential cost/loss function defined as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d4de19b9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}\\exp{(-y_i(f_{m-1}(x_i)+\\beta G(x_i))}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9b9b6dcd",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We optimize $\\beta$ and $G$ for each value of $m=1:M$ as we did in the regression case.\n",
|
||
"This is normally done in two steps. Let us however first rewrite the cost function as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a274021b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}w_i^{m}\\exp{(-y_i\\beta G(x_i))},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fe46199f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where we have defined $w_i^m= \\exp{(-y_if_{m-1}(x_i))}$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "913a48d6",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Building up AdaBoost\n",
|
||
"\n",
|
||
"First, for any $\\beta > 0$, we optimize $G$ by setting"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e707fd99",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"G_m(x) = \\mathrm{sign} \\sum_{i=0}^{n-1} w_i^m I(y_i \\ne G_(x_i)),\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1b749c34",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which is the classifier that minimizes the weighted error rate in predicting $y$.\n",
|
||
"\n",
|
||
"We can do this by rewriting"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b79b881c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\exp{-(\\beta)}\\sum_{y_i=G(x_i)}w_i^m+\\exp{(\\beta)}\\sum_{y_i\\ne G(x_i)}w_i^m,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "115a36fd",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which can be rewritten as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "49086250",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"(\\exp{(\\beta)}-\\exp{-(\\beta)})\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i))+\\exp{(-\\beta)}\\sum_{i=0}^{n-1}w_i^m=0,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a840811a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which leads to"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d213a918",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\beta_m = \\frac{1}{2}\\log{\\frac{1-\\mathrm{\\overline{err}}}{\\mathrm{\\overline{err}}}},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "68a50e61",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where we have redefined the error as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b62cf357",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathrm{\\overline{err}}_m=\\frac{1}{n}\\frac{\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i)}{\\sum_{i=0}^{n-1}w_i^m},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5da2ca5c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"which leads to an update of"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "803e473e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"f_m(x) = f_{m-1}(x) +\\beta_m G_m(x).\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "35e1f9c1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"This leads to the new weights"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3b7c369c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"w_i^{m+1} = w_i^m \\exp{(-y_i\\beta_m G_m(x_i))}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "dae26491",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Adaptive boosting: AdaBoost, Basic Algorithm\n",
|
||
"\n",
|
||
"The algorithm here is rather straightforward. Assume that our weak\n",
|
||
"classifier is a decision tree and we consider a binary set of outputs\n",
|
||
"with $y_i \\in \\{-1,1\\}$ and $i=0,1,2,\\dots,n-1$ as our set of\n",
|
||
"observations. Our design matrix is given in terms of the\n",
|
||
"feature/predictor vectors\n",
|
||
"$\\boldsymbol{X}=[\\boldsymbol{x}_0\\boldsymbol{x}_1\\dots\\boldsymbol{x}_{p-1}]$. Finally, we define also a\n",
|
||
"classifier determined by our data via a function $G(x)$. This function tells us how well we are able to classify our outputs/targets $\\boldsymbol{y}$. \n",
|
||
"\n",
|
||
"We have already defined the misclassification error $\\mathrm{err}$ as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "57668483",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathrm{err}=\\frac{1}{n}\\sum_{i=0}^{n-1}I(y_i\\ne G(x_i)),\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5830f7a2",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where the function $I()$ is one if we misclassify and zero if we classify correctly."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b933bd20",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Basic Steps of AdaBoost\n",
|
||
"\n",
|
||
"With the above definitions we are now ready to set up the algorithm for AdaBoost.\n",
|
||
"The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.\n",
|
||
"1. We start by initializing all weights to $w_i = 1/n$, with $i=0,1,2,\\dots n-1$. It is easy to see that we must have $\\sum_{i=0}^{n-1}w_i = 1$.\n",
|
||
"\n",
|
||
"2. We rewrite the misclassification error as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e8dff87",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"\\mathrm{\\overline{err}}_m=\\frac{\\sum_{i=0}^{n-1}w_i^m I(y_i\\ne G(x_i))}{\\sum_{i=0}^{n-1}w_i},\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1d6a0fa2",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n",
|
||
"\n",
|
||
"a. Fit then a given classifier to the training set using the weights $w_i$.\n",
|
||
"\n",
|
||
"b. Compute then $\\mathrm{err}$ and figure out which events are classified properly and which are classified wrongly.\n",
|
||
"\n",
|
||
"c. Define a quantity $\\alpha_{m} = \\log{(1-\\mathrm{\\overline{err}}_m)/\\mathrm{\\overline{err}}_m}$\n",
|
||
"\n",
|
||
"d. Set the new weights to $w_i = w_i\\times \\exp{(\\alpha_m I(y_i\\ne G(x_i)}$.\n",
|
||
"\n",
|
||
"5. Compute the new classifier $G(x)= \\sum_{i=0}^{n-1}\\alpha_m I(y_i\\ne G(x_i)$.\n",
|
||
"\n",
|
||
"For the iterations with $m \\le 2$ the weights are modified\n",
|
||
"individually at each steps. The observations which were misclassified\n",
|
||
"at iteration $m-1$ have a weight which is larger than those which were\n",
|
||
"classified properly. As this proceeds, the observations which were\n",
|
||
"difficult to classifiy correctly are given a larger influence. Each\n",
|
||
"new classification step $m$ is then forced to concentrate on those\n",
|
||
"observations that are missed in the previous iterations."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eacc9804",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## AdaBoost Examples\n",
|
||
"\n",
|
||
"Using **Scikit-Learn** it is easy to apply the adaptive boosting algorithm, as done here."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "29c9b0a6",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.ensemble import AdaBoostClassifier\n",
|
||
"\n",
|
||
"ada_clf = AdaBoostClassifier(\n",
|
||
" DecisionTreeClassifier(max_depth=2), n_estimators=200,\n",
|
||
" algorithm=\"SAMME.R\", learning_rate=0.01, random_state=42)\n",
|
||
"ada_clf.fit(X_train, y_train)\n",
|
||
"y_pred = ada_clf.predict(X_test)\n",
|
||
"skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
|
||
"plt.show()\n",
|
||
"y_probas = ada_clf.predict_proba(X_test)\n",
|
||
"skplt.metrics.plot_roc(y_test, y_probas)\n",
|
||
"plt.show()\n",
|
||
"skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f5eaa0fc",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Making an ADAboost code yourself"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "1db44432",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Predictions: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"class DecisionStump:\n",
|
||
" def fit(self, X, y, weights):\n",
|
||
" m, n = X.shape\n",
|
||
" self.alpha = 0\n",
|
||
" self.threshold = None\n",
|
||
" self.polarity = 1\n",
|
||
"\n",
|
||
" min_error = float('inf')\n",
|
||
"\n",
|
||
" for feature in range(n):\n",
|
||
" feature_values = np.unique(X[:, feature])\n",
|
||
"\n",
|
||
" for threshold in feature_values:\n",
|
||
" for polarity in [1, -1]:\n",
|
||
" predictions = np.ones(m)\n",
|
||
" predictions[X[:, feature] < threshold] = -1\n",
|
||
" predictions *= polarity\n",
|
||
"\n",
|
||
" error = sum(weights[predictions != y])\n",
|
||
"\n",
|
||
" if error < min_error:\n",
|
||
" min_error = error\n",
|
||
" self.alpha = 0.5 * np.log((1 - error) / (error + 1e-10))\n",
|
||
" self.threshold = threshold\n",
|
||
" self.feature_index = feature\n",
|
||
" self.polarity = polarity\n",
|
||
"\n",
|
||
" def predict(self, X):\n",
|
||
" m = X.shape[0]\n",
|
||
" predictions = np.ones(m)\n",
|
||
" if self.polarity == 1:\n",
|
||
" predictions[X[:, self.feature_index] < self.threshold] = -1\n",
|
||
" else:\n",
|
||
" predictions[X[:, self.feature_index] >= self.threshold] = -1\n",
|
||
" return predictions\n",
|
||
"\n",
|
||
"class AdaBoost:\n",
|
||
" def fit(self, X, y, n_estimators):\n",
|
||
" m = X.shape[0]\n",
|
||
" self.alphas = []\n",
|
||
" self.models = []\n",
|
||
"\n",
|
||
" weights = np.ones(m) / m\n",
|
||
"\n",
|
||
" for _ in range(n_estimators):\n",
|
||
" stump = DecisionStump()\n",
|
||
" stump.fit(X, y, weights)\n",
|
||
" predictions = stump.predict(X)\n",
|
||
"\n",
|
||
" error = sum(weights[predictions != y])\n",
|
||
" if error == 0:\n",
|
||
" break\n",
|
||
"\n",
|
||
" self.models.append(stump)\n",
|
||
" self.alphas.append(stump.alpha)\n",
|
||
"\n",
|
||
" weights *= np.exp(-stump.alpha * y * predictions)\n",
|
||
" weights /= np.sum(weights)\n",
|
||
"\n",
|
||
" def predict(self, X):\n",
|
||
" final_predictions = np.zeros(X.shape[0])\n",
|
||
" for alpha, model in zip(self.alphas, self.models):\n",
|
||
" final_predictions += alpha * model.predict(X)\n",
|
||
" return np.sign(final_predictions)\n",
|
||
"\n",
|
||
"# Example dataset (X, y)\n",
|
||
"X = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]])\n",
|
||
"y = np.array([-1, -1, -1, -1, 1, 1, 1, 1, 1, 1]) # Labels must be -1 or 1\n",
|
||
"\n",
|
||
"# Train AdaBoost\n",
|
||
"ada = AdaBoost()\n",
|
||
"ada.fit(X, y, n_estimators=10)\n",
|
||
"\n",
|
||
"# Predictions\n",
|
||
"predictions = ada.predict(X)\n",
|
||
"print(\"Predictions:\", predictions)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "60fbb085",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent\n",
|
||
"\n",
|
||
"Gradient boosting is again a similar technique to Adaptive boosting,\n",
|
||
"it combines so-called weak classifiers or regressors into a strong\n",
|
||
"method via a series of iterations.\n",
|
||
"\n",
|
||
"In order to understand the method, let us illustrate its basics by\n",
|
||
"bringing back the essential steps in linear regression, where our cost\n",
|
||
"function was the least squares function."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cbc6c37e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## The Squared-Error again! Steepest Descent\n",
|
||
"\n",
|
||
"We start again with our cost function ${\\cal C}(\\boldsymbol{y}m\\boldsymbol{f})=\\sum_{i=0}^{n-1}{\\cal L}(y_i, f(x_i))$ where we want to minimize\n",
|
||
"This means that for every iteration, we need to optimize"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "675390d3",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"(\\hat{\\boldsymbol{f}}) = \\mathrm{argmin}_{\\boldsymbol{f}}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ac24e4fb",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We define a real function $h_m(x)$ that defines our final function $f_M(x)$ as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cfdd6b7f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"f_M(x) = \\sum_{m=0}^M h_m(x).\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "de1524db",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"In the steepest decent approach we approximate $h_m(x) = -\\rho_m g_m(x)$, where $\\rho_m$ is a scalar and $g_m(x)$ the gradient defined as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "506fa7d0",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"g_m(x_i) = \\left[ \\frac{\\partial {\\cal L}(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x_i)=f_{m-1}(x_i)}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cc03bbc9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"With the new gradient we can update $f_m(x) = f_{m-1}(x) -\\rho_m g_m(x)$. Using the above squared-error function we see that\n",
|
||
"the gradient is $g_m(x_i) = -2(y_i-f(x_i))$.\n",
|
||
"\n",
|
||
"Choosing $f_0(x)=0$ we obtain $g_m(x) = -2y_i$ and inserting this into the minimization problem for the cost function we have"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a1145d71",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"(\\rho_1) = \\mathrm{argmin}_{\\rho}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i+2\\rho y_i)^2.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f9bbe14d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Steepest Descent Example\n",
|
||
"\n",
|
||
"Optimizing with respect to $\\rho$ we obtain (taking the derivative) that $\\rho_1 = -1/2$. We have then that"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "44ca0a6c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"f_1(x) = f_{0}(x) -\\rho_1 g_1(x)=-y_i.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7791881d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"We can then proceed and compute"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b3cffde0",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"g_2(x_i) = \\left[ \\frac{\\partial {\\cal L}(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x_i)=f_{1}(x_i)=y_i}=-4y_i,\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "842710e4",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"and find a new value for $\\rho_2=-1/2$ and continue till we have reached $m=M$. We can modify the steepest descent method, or steepest boosting, by introducing what is called **gradient boosting**."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2d0ac22f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Gradient Boosting, algorithm\n",
|
||
"\n",
|
||
"Steepest descent is however not much used, since it only optimizes $f$ at a fixed set of $n$ points,\n",
|
||
"so we do not learn a function that can generalize. However, we can modify the algorithm by\n",
|
||
"fitting a weak learner to approximate the negative gradient signal. \n",
|
||
"\n",
|
||
"Suppose we have a cost function $C(f)=\\sum_{i=0}^{n-1}L(y_i, f(x_i))$ where $y_i$ is our target and $f(x_i)$ the function which is meant to model $y_i$. The above cost function could be our standard squared-error function"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8b7ba0b6",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"C(\\boldsymbol{y},\\boldsymbol{f})=\\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d0ad4149",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"The way we proceed in an iterative fashion is to\n",
|
||
"1. Initialize our estimate $f_0(x)$.\n",
|
||
"\n",
|
||
"2. For $m=1:M$, we\n",
|
||
"\n",
|
||
"a. compute the negative gradient vector $\\boldsymbol{u}_m = -\\partial C(\\boldsymbol{y},\\boldsymbol{f})/\\partial \\boldsymbol{f}(x)$ at $f(x) = f_{m-1}(x)$;\n",
|
||
"\n",
|
||
"b. fit the so-called base-learner to the negative gradient $h_m(u_m,x)$;\n",
|
||
"\n",
|
||
"c. update the estimate $f_m(x) = f_{m-1}(x)+h_m(u_m,x)$;\n",
|
||
"\n",
|
||
"4. The final estimate is then $f_M(x) = \\sum_{m=1}^M h_m(u_m,x)$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6efc5e2e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Gradient Boosting, Examples of Regression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "4a6a7ba3",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Max depth: 1\n",
|
||
"Error: 0.4010613825254484\n",
|
||
"Bias^2: 0.2079593417034804\n",
|
||
"Var: 0.19310204082196794\n",
|
||
"0.4010613825254484 >= 0.2079593417034804 + 0.19310204082196794 = 0.4010613825254483\n",
|
||
"Max depth: 2\n",
|
||
"Error: 0.4250776117755916\n",
|
||
"Bias^2: 0.2080984218270197\n",
|
||
"Var: 0.21697918994857185\n",
|
||
"0.4250776117755916 >= 0.2080984218270197 + 0.21697918994857185 = 0.42507761177559156\n",
|
||
"Max depth: 3\n",
|
||
"Error: 0.4250796355306808\n",
|
||
"Bias^2: 0.2080985447081304\n",
|
||
"Var: 0.21698109082255032\n",
|
||
"0.4250796355306808 >= 0.2080985447081304 + 0.21698109082255032 = 0.42507963553068073\n",
|
||
"Max depth: 4\n",
|
||
"Error: 0.4250796355306808\n",
|
||
"Bias^2: 0.2080985447081304\n",
|
||
"Var: 0.21698109082255038\n",
|
||
"0.4250796355306808 >= 0.2080985447081304 + 0.21698109082255038 = 0.4250796355306808\n",
|
||
"Max depth: 5\n",
|
||
"Error: 0.42507963553068073\n",
|
||
"Bias^2: 0.2080985447081304\n",
|
||
"Var: 0.21698109082255032\n",
|
||
"0.42507963553068073 >= 0.2080985447081304 + 0.21698109082255032 = 0.42507963553068073\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
||
" y = column_or_1d(y, warn=True)\n",
|
||
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
||
" y = column_or_1d(y, warn=True)\n",
|
||
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
||
" y = column_or_1d(y, warn=True)\n",
|
||
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
||
" y = column_or_1d(y, warn=True)\n",
|
||
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
|
||
" y = column_or_1d(y, warn=True)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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t19J0uv16vKgXABBgevzt0EuHSxFfXy/r1AdiJ9q0+6HaRW06oVnSlyEhKg0NUUloqEpDQ1QaGqrS0FAdDwn22C/EspTS1Kz0pialNzVrUFOT6+99nGWS9l5wbQCAHq7B9/9jalZwaaqVgnrm5YiTQUHthpOy0BA1n+MSzSXNjnbDyYDmZsMmHwAA04LLT7ZJsTFnHGjnA7vNh7jtPL/3VZtO1HJWmyZnk8pqvlTpqXKV1JSr9FS5jtR8rtJT5TrZ6Hm5LTwoTKkxA5Uek6L0mFQN+vrPtOiBig2L8euYOvY+Hs4DADCb3S4tTvLpKTsVXFasWKHHH39cFRUVGjNmjJYtW6bx48e32/a5557Tiy++qH379kmSxo4dq1/96lce259T/CApNrYzJV80LuS24qTIpHY3xvaL6sdtxQCAi0+o77++xevgsm7dOuXn52vlypWaMGGCioqKlJOTo4MHD6pv375t2m/evFm33HKLJk6cqIiICC1ZskTXXXedPv74Yw0YMMAng7gYnW4+rTJ7mftdO9WlOmI/opqmGo/92rutOC02TWmxadxWDADo8WyW5d0tHRMmTNC4ceO0fPlySZLT6VRKSoruvfdezZ8//7z9HQ6H+vTpo+XLl2vGjBkdek+73a64uDhVV1cr9iJacXFaTlXWVrYJJ6X2Uh2tPeqxH7cVAwB6gq74/PZqxaWxsVG7du1SQUGB61hQUJCys7O1Y8eODp2jrq5OTU1Nio+P99imoaFBDQ3f3K5rt/v+dipv1DTW6EjrbcVnrZ7UO+o99osNi20bTmLTlRqb6n5bMQAA6BCvgsvx48flcDiUlOS+0SYpKUmffPJJh85x//33q3///srOzvbYprCwUA8//LA3pV2wZmezvqz5ss3ekyP2I6o6XeWxX4gtRANjBro9LbZ19aRPeB9WTwAA8KFuvato8eLFWrt2rTZv3qyIiAiP7QoKCpSfn+/62W63KyUlxSc1nKw/2e7G2PJT5WpyNnnsd0nEJd+Ekq+DSXpsugbEDFBoUKhPagMAAOfmVXBJSEhQcHCwKisr3Y5XVlYqOTn5nH2XLl2qxYsX680339To0aPP2TY8PFzh4eHelOamydGk8lPl7e49Odlw0vP7BocrNTbVFU7O/Lbi2LCLZ28NAAA9lVfBJSwsTGPHjlVxcbGmT58uqWVzbnFxsebMmeOx369//Ws99thj2rhxozIzMy+o4FaWZelE/Yk2Kyel1S23FTssh8e+3FYMAICZvL5UlJ+fr5kzZyozM1Pjx49XUVGRamtrNWvWLEnSjBkzNGDAABUWFkqSlixZooULF+qll15Senq6KipavsQvOjpa0dHRXr33qr2rVOmsdIWU891WnBab1mbvCbcVAwBgLq+DS25urqqqqrRw4UJVVFQoIyNDGzZscG3YLSsrU1DQN6sWzzzzjBobG/Uf//EfbudZtGiRHnroIa/e+7d7f6vgXt98/87ZtxWfufekb2RfNsYCABBgvH6Oiz+03gf+sw0/04j+I1whJSU2ReHBnd8LAwAAuo7fn+PibwuzFl5UD6ADAADdi52oAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMEangsuKFSuUnp6uiIgITZgwQe+//77Hth9//LG+//3vKz09XTabTUVFRZ2tFQAA9HBeB5d169YpPz9fixYt0u7duzVmzBjl5OTo2LFj7bavq6vT4MGDtXjxYiUnJ19wwQAAoOfyOrg8+eSTuuOOOzRr1ixddtllWrlypSIjI7Vq1ap2248bN06PP/64br75ZoWHh19wwQAAoOfyKrg0NjZq165dys7O/uYEQUHKzs7Wjh07fFZUQ0OD7Ha72wsAAMCr4HL8+HE5HA4lJSW5HU9KSlJFRYXPiiosLFRcXJzrlZKS4rNzAwAAc12UdxUVFBSourra9SovL/d3SQAA4CIQ4k3jhIQEBQcHq7Ky0u14ZWWlTzfehoeHsx8GAAC04dWKS1hYmMaOHavi4mLXMafTqeLiYmVlZfm8OAAAgDN5teIiSfn5+Zo5c6YyMzM1fvx4FRUVqba2VrNmzZIkzZgxQwMGDFBhYaGklg29+/fvd/39iy++0J49exQdHa2hQ4f6cCgAACDQeR1ccnNzVVVVpYULF6qiokIZGRnasGGDa8NuWVmZgoK+Wcj58ssv9a1vfcv189KlS7V06VJNmjRJmzdvvvARAACAHsNmWZbl7yLOx263Ky4uTtXV1YqNjfV3OQAAoAO64vP7oryrCAAAoD0EFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4AAAAYxBcAACAMQguAADAGAQXAABgDIILAAAwBsEFAAAYg+ACAACMQXABAADGCPF3Ab7idDrV2Njo7zLQAaGhoQoODvZ3GQAAAwVEcGlsbFRJSYmcTqe/S0EH9e7dW8nJybLZbP4uBQBgEOODi2VZOnr0qIKDg5WSkqKgIK5+Xcwsy1JdXZ2OHTsmSerXr5+fKwIAmMT44NLc3Ky6ujr1799fkZGR/i4HHdCrVy9J0rFjx9S3b18uGwEAOsz45QmHwyFJCgsL83Ml8EZryGxqavJzJQAAkxgfXFqxV8IszBcAoDMCJrgAAIDAR3ABAADGILj4yY9+9CPZbLY2rylTpvi7NAAALlrG31VksilTpuiFF15wOxYeHt5u26amJoWGhroda2xs7NSm5M72AwDA31hx8aPw8HAlJye7vfr06SOpZfPqM888oxtvvFFRUVF67LHH9NBDDykjI0PPP/+8Bg0apIiICElSWVmZpk2bpujoaMXGxuqmm25SZWWl63089QMAwDQBt+JiWZZONzn88t69QoN9erfMQw89pMWLF6uoqEghISFatWqVPvvsM/3P//yP/vKXvyg4OFhOp9MVWrZs2aLm5mbNnj1bubm52rx5s+tcZ/cDAMBEARdcTjc5dNnCjX557/2/zFFkWMf/SV977TVFR0e7HXvggQf0wAMPSJJ+8IMfaNasWW6/b2xs1IsvvqjExERJ0qZNm7R3716VlJQoJSVFkvTiiy/q8ssv186dOzVu3Lh2+wEAYKKACy4mueaaa/TMM8+4HYuPj3f9PTMzs02ftLQ0t/Bx4MABpaSkuEKLJF122WXq3bu3Dhw44AouZ/cDAMBEARdceoUGa/8vc/z23t6IiorS0KFDz/n7jhzr6HsBAGC6gAsuNpvNq8s1phs5cqTKy8tVXl7uWnXZv3+/Tp48qcsuu8zP1QEA4Fs95xP+ItTQ0KCKigq3YyEhIUpISOjwObKzszVq1CjdeuutKioqUnNzs+655x5NmjSp3UtNAACYjNuh/WjDhg3q16+f2+tf//VfvTqHzWbTX//6V/Xp00dXX321srOzNXjwYK1bt66LqgYAwH9slmVZ/i7ifOx2u+Li4lRdXa3Y2Fi339XX16ukpITnkxiGeQOAwHeuz+/OYsUFAAAYg+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4OInkydP1rx58zz+Pj09XUVFRd1WDwAAJuBLFi9SO3fuVFRUlL/LAADgokJwuUglJib6uwQAAC46XCryo+bmZs2ZM0dxcXFKSEjQggUL1Pqdl2dfKnryySc1atQoRUVFKSUlRffcc49qampcvz9y5IhuuOEG9enTR1FRUbr88sv1xhtvdPeQAADoUoG34mJZUlOdf947NFKy2Trc/Pe//73y8vL0/vvv64MPPtCdd96p1NRU3XHHHW3aBgUF6Te/+Y0GDRqkw4cP65577tF9992n//7v/5YkzZ49W42Njdq6dauioqK0f/9+RUdH+2xoAABcDAIvuDTVSb/q75/3fuBLKazj+1JSUlL01FNPyWazafjw4dq7d6+eeuqpdoPLmRt509PT9eijj+quu+5yBZeysjJ9//vf16hRoyRJgwcPvrCxAABwEeJSkR/9y7/8i2xnrNBkZWXp008/lcPhaNP2zTff1LXXXqsBAwYoJiZGt912m06cOKG6upbVpZ/+9Kd69NFHddVVV2nRokX66KOPum0cAAB0l8BbcQmNbFn58Nd7d4HS0lJdf/31uvvuu/XYY48pPj5eb7/9tvLy8tTY2KjIyEjdfvvtysnJ0euvv66//e1vKiws1BNPPKF77723S2oCAMAfAi+42GxeXa7xp/fee8/t53fffVfDhg1TcHCw2/Fdu3bJ6XTqiSeeUFBQyyLZyy+/3OZ8KSkpuuuuu3TXXXepoKBAzz33HMEFABBQuFTkR2VlZcrPz9fBgwf1pz/9ScuWLdPcuXPbtBs6dKiampq0bNkyHT58WGvWrNHKlSvd2sybN08bN25USUmJdu/erbfeeksjR47srqEAANAtCC5+NGPGDJ0+fVrjx4/X7NmzNXfuXN15551t2o0ZM0ZPPvmklixZoiuuuEJ//OMfVVhY6NbG4XBo9uzZGjlypKZMmaJLL73UtXEXAIBAYbNaHxxyEbPb7YqLi1N1dbViY2PdfldfX6+SkhINGjRIERERfqoQ3mLeACDwnevzu7NYcQEAAMYguAAAAGMQXAAAgDEILgAAwBgEFwAAYAyCCwAAMAbBBQAAGIPgAgAAjEFwAQAAxiC4BBibzab169f7uwwAALoEwcUPbrjhBk2ZMqXd323btk02m00fffRRp8599OhRffe7372Q8gAAuGgRXPwgLy9PmzZt0ueff97mdy+88IIyMzM1evRor87Z2NgoSUpOTlZ4eLhP6gQA4GJDcPGD66+/XomJiVq9erXb8ZqaGr3yyiuaPn26brnlFg0YMECRkZEaNWqU/vSnP7m1nTx5subMmaN58+YpISFBOTk5ktpeKrr//vt16aWXKjIyUoMHD9aCBQvU1NTk+v1DDz2kjIwMrVmzRunp6YqLi9PNN9+sU6dOudo4nU79+te/1tChQxUeHq7U1FQ99thjrt+Xl5frpptuUu/evRUfH69p06aptLTUd/9gAAB8LeCCi2VZqmuq88uro1+0HRISohkzZmj16tVufV555RU5HA798Ic/1NixY/X6669r3759uvPOO3Xbbbfp/fffdzvP73//e4WFhWn79u1auXJlu+8VExOj1atXa//+/Xr66af13HPP6amnnnJrc+jQIa1fv16vvfaaXnvtNW3ZskWLFy92/b6goECLFy/WggULtH//fr300ktKSkqSJDU1NSknJ0cxMTHatm2btm/frujoaE2ZMsW1CgQAgK/YrI5+2p5hxYoVevzxx1VRUaExY8Zo2bJlGj9+vMf2r7zyihYsWKDS0lINGzZMS5Ys0fe+970Ov9+5vha7vr5eJSUlGjRokCIiIlTXVKcJL03wdkg+8d4P3lNkaGSH2n7yyScaOXKk3nrrLU2ePFmSdPXVVystLU1r1qxp0/7666/XiBEjtHTpUkktKy52u127d+92a2ez2fTqq69q+vTp7b7v0qVLtXbtWn3wwQeSWlZcWucyJiZGknTfffdp69atevfdd3Xq1CklJiZq+fLluv3229uc7w9/+IMeffRRHThwQDabTVLLZavevXtr/fr1uu6669qt4+x5AwAEnnN9fneW1ysu69atU35+vhYtWqTdu3drzJgxysnJ0bFjx9pt/8477+iWW25RXl6ePvzwQ02fPl3Tp0/Xvn37Lrh4k40YMUITJ07UqlWrJEmfffaZtm3bpry8PDkcDj3yyCMaNWqU4uPjFR0drY0bN6qsrMztHGPHjj3v+6xbt05XXXWVkpOTFR0drQcffLDNedLT012hRZL69evnms8DBw6ooaFB1157bbvn/8c//qHPPvtMMTExio6OVnR0tOLj41VfX69Dhw559W8CAMD5hHjb4cknn9Qdd9yhWbNmSZJWrlyp119/XatWrdL8+fPbtH/66ac1ZcoU/fznP5ckPfLII9q0aZOWL1/u8fLGhegV0kvv/eA9n5+3o+/tjby8PN17771asWKFXnjhBQ0ZMkSTJk3SkiVL9PTTT6uoqEijRo1SVFSU5s2b1+bSS1RU1DnPv2PHDt166616+OGHlZOTo7i4OK1du1ZPPPGEW7vQ0FC3n202m5xOZ8uYep17TDU1NRo7dqz++Mc/tvldYmLiOfsCAOAtr4JLY2Ojdu3apYKCAtexoKAgZWdna8eOHe322bFjh/Lz892O5eTknPNZIw0NDWpoaHD9bLfbO1yjzWbr8OUaf7vppps0d+5cvfTSS3rxxRd19913y2azafv27Zo2bZp++MMfSmrZHPt///d/uuyyy7w6/zvvvKO0tDT94he/cB07cuSIV+cYNmyYevXqpeLi4nYvFV155ZVat26d+vbt67NlQAAAPPHqUtHx48flcDhcGzNbJSUlqaKiot0+FRUVXrWXpMLCQsXFxbleKSkp3pRpjOjoaOXm5qqgoEBHjx7Vj370I0ktYWHTpk165513dODAAf3kJz9RZWWl1+cfNmyYysrKtHbtWh06dEi/+c1v9Oqrr3p1joiICN1///2677779OKLL+rQoUN699139bvf/U6SdOuttyohIUHTpk3Ttm3bVFJSos2bN+unP/1pu7d7AwBwIS7Ku4oKCgpUXV3tepWXl/u7pC6Tl5enr776Sjk5Oerfv78k6cEHH9SVV16pnJwcTZ48WcnJyR43257LjTfeqP/8z//UnDlzlJGRoXfeeUcLFizw+jwLFizQz372My1cuFAjR45Ubm6uaw9MZGSktm7dqtTUVP37v/+7Ro4cqby8PNXX17MCAwDwOa/uKmpsbFRkZKT+/Oc/u32Qzpw5UydPntRf//rXNn1SU1OVn5+vefPmuY4tWrRI69ev1z/+8Y8Ova83dxXBDMwbAAQ+v99VFBYWprFjx6q4uNh1zOl0qri4WFlZWe32ycrKcmsvSZs2bfLYHgAAwBOv7yrKz8/XzJkzlZmZqfHjx6uoqEi1tbWuu4xmzJihAQMGqLCwUJI0d+5cTZo0SU888YSmTp3qeobIb3/7W9+OBAAABDyvg0tubq6qqqq0cOFCVVRUKCMjQxs2bHBtwC0rK1NQ0DcLORMnTtRLL72kBx98UA888ICGDRum9evX64orrvDdKAAAQI/QqSfndjf2uAQe5g0AAp/f97gAAAD4U8AEFwMWjnCG1ifzAgDgDa/3uFxsQkNDZbPZVFVVpcTERNcX/eHiZFmWGhsbVVVVpaCgIIWFhfm7JACAQYwPLsHBwRo4cKA+//xzlZaW+rscdFBkZKRSU1PdNnIDAHA+xgcXqeXR+cOGDVNTU5O/S0EHBAcHKyQkhNUxAIDXAiK4SC0fhsHBwf4uAwAAdCHW6QEAgDEILgAAwBgEFwAAYAwj9ri0PqPFbrf7uRIAANBRrZ/bvnzWmhHB5cSJE5KklJQUP1cCAAC8deLECcXFxfnkXEYEl/j4eEktX+Doq4GbwG63KyUlReXl5T77jgcTMG7G3RMwbsbdE1RXVys1NdX1Oe4LRgSX1oeUxcXF9agJbxUbG8u4exDG3bMw7p6lp47blw8bZXMuAAAwBsEFAAAYw4jgEh4erkWLFik8PNzfpXQrxs24ewLGzbh7Asbtu3HbLF/eowQAANCFjFhxAQAAkAguAADAIAQXAABgDIILAAAwht+Dy9atW3XDDTeof//+stlsWr9+/Xn7bN68WVdeeaXCw8M1dOhQrV69usvr9DVvx71582bZbLY2r4qKiu4p2EcKCws1btw4xcTEqG/fvpo+fboOHjx43n6vvPKKRowYoYiICI0aNUpvvPFGN1TrO50Z9+rVq9vMd0RERDdV7BvPPPOMRo8e7XroVlZWlv73f//3nH1Mn2vJ+3EHwlyfbfHixbLZbJo3b9452wXCfJ+pI+MOlPl+6KGH2oxjxIgR5+zji/n2e3Cpra3VmDFjtGLFig61Lykp0dSpU3XNNddoz549mjdvnm6//XZt3Lixiyv1LW/H3ergwYM6evSo69W3b98uqrBrbNmyRbNnz9a7776rTZs2qampSdddd51qa2s99nnnnXd0yy23KC8vTx9++KGmT5+u6dOna9++fd1Y+YXpzLillqdsnjnfR44c6aaKfWPgwIFavHixdu3apQ8++ED/9m//pmnTpunjjz9ut30gzLXk/bgl8+f6TDt37tSzzz6r0aNHn7NdoMx3q46OWwqc+b788svdxvH22297bOuz+bYuIpKsV1999Zxt7rvvPuvyyy93O5abm2vl5OR0YWVdqyPjfuuttyxJ1ldffdUtNXWXY8eOWZKsLVu2eGxz0003WVOnTnU7NmHCBOsnP/lJV5fXZToy7hdeeMGKi4vrvqK6SZ8+faznn3++3d8F4ly3Ote4A2muT506ZQ0bNszatGmTNWnSJGvu3Lke2wbSfHsz7kCZ70WLFlljxozpcHtfzbffV1y8tWPHDmVnZ7sdy8nJ0Y4dO/xUUffKyMhQv3799J3vfEfbt2/3dzkXrLq6WpLO+QVcgTjnHRm3JNXU1CgtLU0pKSnn/T/2i53D4dDatWtVW1urrKysdtsE4lx3ZNxS4Mz17NmzNXXq1Dbz2J5Amm9vxi0Fznx/+umn6t+/vwYPHqxbb71VZWVlHtv6ar6N+JLFM1VUVCgpKcntWFJSkux2u06fPq1evXr5qbKu1a9fP61cuVKZmZlqaGjQ888/r8mTJ+u9997TlVde6e/yOsXpdGrevHm66qqrdMUVV3hs52nOTdvf06qj4x4+fLhWrVql0aNHq7q6WkuXLtXEiRP18ccfa+DAgd1Y8YXZu3evsrKyVF9fr+joaL366qu67LLL2m0bSHPtzbgDZa7Xrl2r3bt3a+fOnR1qHyjz7e24A2W+J0yYoNWrV2v48OE6evSoHn74YX3729/Wvn37FBMT06a9r+bbuODSUw0fPlzDhw93/Txx4kQdOnRITz31lNasWePHyjpv9uzZ2rdv3zmviQaijo47KyvL7f/QJ06cqJEjR+rZZ5/VI4880tVl+szw4cO1Z88eVVdX689//rNmzpypLVu2ePwQDxTejDsQ5rq8vFxz587Vpk2bjNxo2lmdGXcgzLckffe733X9ffTo0ZowYYLS0tL08ssvKy8vr8ve17jgkpycrMrKSrdjlZWVio2NDdjVFk/Gjx9v7If+nDlz9Nprr2nr1q3n/T8MT3OenJzclSV2CW/GfbbQ0FB961vf0meffdZF1XWNsLAwDR06VJI0duxY7dy5U08//bSeffbZNm0Daa69GffZTJzrXbt26dixY24rwA6HQ1u3btXy5cvV0NCg4OBgtz6BMN+dGffZTJzv9vTu3VuXXnqpx3H4ar6N2+OSlZWl4uJit2ObNm0657XjQLVnzx7169fP32V4xbIszZkzR6+++qr+/ve/a9CgQeftEwhz3plxn83hcGjv3r3GzfnZnE6nGhoa2v1dIMy1J+ca99lMnOtrr71We/fu1Z49e1yvzMxM3XrrrdqzZ0+7H96BMN+dGffZTJzv9tTU1OjQoUMex+Gz+fZqK28XOHXqlPXhhx9aH374oSXJevLJJ60PP/zQOnLkiGVZljV//nzrtttuc7U/fPiwFRkZaf385z+3Dhw4YK1YscIKDg62NmzY4K8hdIq3437qqaes9evXW59++qm1d+9ea+7cuVZQUJD15ptv+msInXL33XdbcXFx1ubNm62jR4+6XnV1da42t912mzV//nzXz9u3b7dCQkKspUuXWgcOHLAWLVpkhYaGWnv37vXHEDqlM+N++OGHrY0bN1qHDh2ydu3aZd18881WRESE9fHHH/tjCJ0yf/58a8uWLVZJSYn10UcfWfPnz7dsNpv1t7/9zbKswJxry/J+3IEw1+05++6aQJ3vs51v3IEy3z/72c+szZs3WyUlJdb27dut7OxsKyEhwTp27JhlWV03334PLq23+Z79mjlzpmVZljVz5kxr0qRJbfpkZGRYYWFh1uDBg60XXnih2+u+UN6Oe8mSJdaQIUOsiIgIKz4+3po8ebL197//3T/FX4D2xizJbQ4nTZrk+ndo9fLLL1uXXnqpFRYWZl1++eXW66+/3r2FX6DOjHvevHlWamqqFRYWZiUlJVnf+973rN27d3d/8Rfgxz/+sZWWlmaFhYVZiYmJ1rXXXuv68LaswJxry/J+3IEw1+05+wM8UOf7bOcbd6DMd25urtWvXz8rLCzMGjBggJWbm2t99tlnrt931XzbLMuyvFujAQAA8A/j9rgAAICei+ACAACMQXABAADGILgAAABjEFwAAIAxCC4AAMAYBBcAAGAMggsAADAGwQUAABiD4AIAAIxBcAEAAMYguAAAAGP8fxOYp3tSWdlYAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn.ensemble import GradientBoostingRegressor\n",
|
||
"import scikitplot as skplt\n",
|
||
"from sklearn.metrics import mean_squared_error\n",
|
||
"\n",
|
||
"n = 100\n",
|
||
"maxdegree = 6\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",
|
||
"\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(1,maxdegree):\n",
|
||
" model = GradientBoostingRegressor(max_depth=degree, n_estimators=100, learning_rate=1.0) \n",
|
||
" model.fit(X_train,y_train)\n",
|
||
" y_pred = model.predict(X_test)\n",
|
||
" polydegree[degree] = degree\n",
|
||
" error[degree] = np.mean( np.mean((y_test - y_pred)**2) )\n",
|
||
" bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )\n",
|
||
" variance[degree] = np.mean( np.var(y_pred) )\n",
|
||
" print('Max depth:', 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.xlim(1,maxdegree-1)\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": "23c85cdf",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Gradient Boosting, Classification Example"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "777b44f6",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(426, 30)\n",
|
||
"(143, 30)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[0.93333333 0.93333333 0.86666667 1. 1. 0.92857143\n",
|
||
" 1. 0.92857143 0.85714286 0.92857143]\n",
|
||
"Test set accuracy with Gradient boosting and scaled data: 0.97\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.model_selection import train_test_split \n",
|
||
"from sklearn.datasets import load_breast_cancer\n",
|
||
"import scikitplot as skplt\n",
|
||
"from sklearn.ensemble import GradientBoostingClassifier\n",
|
||
"from sklearn.model_selection import cross_validate\n",
|
||
"\n",
|
||
"# Load the data\n",
|
||
"cancer = load_breast_cancer()\n",
|
||
"\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
|
||
"print(X_train.shape)\n",
|
||
"print(X_test.shape)\n",
|
||
"#now scale the data\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"scaler = StandardScaler()\n",
|
||
"scaler.fit(X_train)\n",
|
||
"X_train_scaled = scaler.transform(X_train)\n",
|
||
"X_test_scaled = scaler.transform(X_test)\n",
|
||
"\n",
|
||
"gd_clf = GradientBoostingClassifier(max_depth=3, n_estimators=100, learning_rate=1.0) \n",
|
||
"gd_clf.fit(X_train_scaled, y_train)\n",
|
||
"#Cross validation\n",
|
||
"accuracy = cross_validate(gd_clf,X_test_scaled,y_test,cv=10)['test_score']\n",
|
||
"print(accuracy)\n",
|
||
"print(\"Test set accuracy with Gradient boosting and scaled data: {:.2f}\".format(gd_clf.score(X_test_scaled,y_test)))\n",
|
||
"\n",
|
||
"import scikitplot as skplt\n",
|
||
"y_pred = gd_clf.predict(X_test_scaled)\n",
|
||
"skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
|
||
"plt.show()\n",
|
||
"y_probas = gd_clf.predict_proba(X_test_scaled)\n",
|
||
"skplt.metrics.plot_roc(y_test, y_probas)\n",
|
||
"plt.show()\n",
|
||
"skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "79c731f6",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## XGBoost: Extreme Gradient Boosting\n",
|
||
"\n",
|
||
"[XGBoost](https://github.com/dmlc/xgboost) or Extreme Gradient\n",
|
||
"Boosting, is an optimized distributed gradient boosting library\n",
|
||
"designed to be highly efficient, flexible and portable. It implements\n",
|
||
"machine learning algorithms under the Gradient Boosting\n",
|
||
"framework. XGBoost provides a parallel tree boosting that solve many\n",
|
||
"data science problems in a fast and accurate way. See the [article by Chen and Guestrin](https://arxiv.org/abs/1603.02754).\n",
|
||
"\n",
|
||
"The authors design and build a highly scalable end-to-end tree\n",
|
||
"boosting system. It has a theoretically justified weighted quantile\n",
|
||
"sketch for efficient proposal calculation. It introduces a novel sparsity-aware algorithm for parallel tree learning and an effective cache-aware block structure for out-of-core tree learning.\n",
|
||
"\n",
|
||
"It is now the algorithm which wins essentially all ML competitions!!!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "abf51c81",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Xgboost on the Cancer Data\n",
|
||
"\n",
|
||
"As you will see from the confusion matrix below, XGBoots does an excellent job on the Wisconsin cancer data and outperforms essentially all agorithms we have discussed till now."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "3243120a",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(426, 30)\n",
|
||
"(143, 30)\n",
|
||
"Test set accuracy with Gradient Boosting and scaled data: 1.00\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
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"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 5000x1000 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.model_selection import train_test_split \n",
|
||
"from sklearn.datasets import load_breast_cancer\n",
|
||
"from sklearn.preprocessing import LabelEncoder\n",
|
||
"from sklearn.model_selection import cross_validate\n",
|
||
"import scikitplot as skplt\n",
|
||
"import xgboost as xgb\n",
|
||
"# Load the data\n",
|
||
"cancer = load_breast_cancer()\n",
|
||
"\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
|
||
"print(X_train.shape)\n",
|
||
"print(X_test.shape)\n",
|
||
"#now scale the data\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"scaler = StandardScaler()\n",
|
||
"scaler.fit(X_train)\n",
|
||
"X_train_scaled = scaler.transform(X_train)\n",
|
||
"X_test_scaled = scaler.transform(X_test)\n",
|
||
"\n",
|
||
"xg_clf = xgb.XGBClassifier()\n",
|
||
"xg_clf.fit(X_train_scaled,y_train)\n",
|
||
"\n",
|
||
"y_test = xg_clf.predict(X_test_scaled)\n",
|
||
"\n",
|
||
"print(\"Test set accuracy with Gradient Boosting and scaled data: {:.2f}\".format(xg_clf.score(X_test_scaled,y_test)))\n",
|
||
"\n",
|
||
"import scikitplot as skplt\n",
|
||
"y_pred = xg_clf.predict(X_test_scaled)\n",
|
||
"skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
|
||
"plt.show()\n",
|
||
"y_probas = xg_clf.predict_proba(X_test_scaled)\n",
|
||
"skplt.metrics.plot_roc(y_test, y_probas)\n",
|
||
"plt.show()\n",
|
||
"skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
"xgb.plot_tree(xg_clf,num_trees=0)\n",
|
||
"plt.rcParams['figure.figsize'] = [50, 10]\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"xgb.plot_importance(xg_clf)\n",
|
||
"plt.rcParams['figure.figsize'] = [5, 5]\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3415611e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Gradient boosting, making our own code for a regression case"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "0a1059eb",
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"editable": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Predictions: [1.49999399 1.69995637 3.49991627 3.6998795 4.99984484]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"class DecisionTreeRegressor:\n",
|
||
" def __init__(self, max_depth=3):\n",
|
||
" self.max_depth = max_depth\n",
|
||
" self.tree = None\n",
|
||
" def fit(self, X, y):\n",
|
||
" self.tree = self._grow_tree(X, y)\n",
|
||
" def _grow_tree(self, X, y, depth=0):\n",
|
||
" n_samples, n_features = X.shape\n",
|
||
" if depth < self.max_depth:\n",
|
||
" best_feature, best_threshold = self._best_split(X, y)\n",
|
||
" if best_feature is not None:\n",
|
||
" left_indices = X[:, best_feature] < best_threshold\n",
|
||
" right_indices = X[:, best_feature] >= best_threshold\n",
|
||
" left_child = self._grow_tree(X[left_indices], y[left_indices], depth + 1)\n",
|
||
" right_child = self._grow_tree(X[right_indices], y[right_indices], depth + 1)\n",
|
||
" return (best_feature, best_threshold, left_child, right_child)\n",
|
||
" return np.mean(y)\n",
|
||
" def _best_split(self, X, y):\n",
|
||
" best_mse = float('inf')\n",
|
||
" best_feature, best_threshold = None, None\n",
|
||
" n_samples, n_features = X.shape\n",
|
||
" \n",
|
||
" for feature in range(n_features):\n",
|
||
" thresholds = np.unique(X[:, feature])\n",
|
||
" for threshold in thresholds:\n",
|
||
" left_indices = X[:, feature] < threshold\n",
|
||
" right_indices = X[:, feature] >= threshold\n",
|
||
" if len(y[left_indices]) > 0 and len(y[right_indices]) > 0:\n",
|
||
" left_mse = np.mean((y[left_indices] - np.mean(y[left_indices])) ** 2)\n",
|
||
" right_mse = np.mean((y[right_indices] - np.mean(y[right_indices])) ** 2)\n",
|
||
" mse = (len(y[left_indices]) * left_mse + len(y[right_indices]) * right_mse) / n_samples\n",
|
||
" \n",
|
||
" if mse < best_mse:\n",
|
||
" best_mse = mse\n",
|
||
" best_feature = feature\n",
|
||
" best_threshold = threshold\n",
|
||
" return best_feature, best_threshold\n",
|
||
" def predict(self, X):\n",
|
||
" return np.array([self._predict_sample(sample, self.tree) for sample in X])\n",
|
||
" def _predict_sample(self, sample, node):\n",
|
||
" if isinstance(node, tuple):\n",
|
||
" feature, threshold, left_child, right_child = node\n",
|
||
" if sample[feature] < threshold:\n",
|
||
" return self._predict_sample(sample, left_child)\n",
|
||
" else:\n",
|
||
" return self._predict_sample(sample, right_child)\n",
|
||
" return node\n",
|
||
"class GradientBoostingRegressor:\n",
|
||
" def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):\n",
|
||
" self.n_estimators = n_estimators\n",
|
||
" self.learning_rate = learning_rate\n",
|
||
" self.max_depth = max_depth\n",
|
||
" self.models = []\n",
|
||
" def fit(self, X, y):\n",
|
||
" y_pred = np.zeros(y.shape)\n",
|
||
" for _ in range(self.n_estimators):\n",
|
||
" residuals = y - y_pred\n",
|
||
" model = DecisionTreeRegressor(max_depth=self.max_depth)\n",
|
||
" model.fit(X, residuals)\n",
|
||
" y_pred += self.learning_rate * model.predict(X)\n",
|
||
" self.models.append(model)\n",
|
||
" def predict(self, X):\n",
|
||
" y_pred = np.zeros(X.shape[0])\n",
|
||
" for model in self.models:\n",
|
||
" y_pred += self.learning_rate * model.predict(X)\n",
|
||
" return y_pred\n",
|
||
"# Example usage\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" # Sample data\n",
|
||
" X = np.array([[1], [2], [3], [4], [5]])\n",
|
||
" y = np.array([1.5, 1.7, 3.5, 3.7, 5.0])\n",
|
||
" model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=2)\n",
|
||
" model.fit(X, y)\n",
|
||
" predictions = model.predict(X)\n",
|
||
" print(\"Predictions:\", predictions)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e88f148c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Summary of course"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "05417d95",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## What? Me worry? No final exam in this course!\n",
|
||
"<!-- dom:FIGURE: [figures/exam1.jpeg, width=500 frac=0.6] -->\n",
|
||
"<!-- begin figure -->\n",
|
||
"\n",
|
||
"<img src=\"figures/exam1.jpeg\" width=\"500\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
||
"<!-- end figure -->"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "07dabfb9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Topics we have covered this year\n",
|
||
"\n",
|
||
"The course has two central parts\n",
|
||
"\n",
|
||
"1. Statistical analysis and optimization of data\n",
|
||
"\n",
|
||
"2. Machine learning"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f4be6096",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Statistical analysis and optimization of data\n",
|
||
"\n",
|
||
"The following topics have been discussed:\n",
|
||
"1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;\n",
|
||
"\n",
|
||
"2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;\n",
|
||
"\n",
|
||
"3. Central elements from linear algebra, matrix inversion and SVD\n",
|
||
"\n",
|
||
"4. Gradient methods for data optimization\n",
|
||
"\n",
|
||
"5. Estimation of errors using cross-validation, bootstrapping and jackknife methods;\n",
|
||
"\n",
|
||
"6. Practical optimization using Singular-value decomposition and least squares for parameterizing data.\n",
|
||
"\n",
|
||
"7. Not discussed: Principal Component Analysis to reduce the number of features."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "13fbfb78",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Machine learning\n",
|
||
"\n",
|
||
"* Linear methods for regression and classification:\n",
|
||
"\n",
|
||
"a. Ordinary Least Squares\n",
|
||
"\n",
|
||
"b. Ridge regression\n",
|
||
"\n",
|
||
"c. Lasso regression\n",
|
||
"\n",
|
||
"d. Logistic regression\n",
|
||
"\n",
|
||
"* Neural networks and deep learning:\n",
|
||
"\n",
|
||
"a. Feed Forward Neural Networks\n",
|
||
"\n",
|
||
"b. Convolutional Neural Networks\n",
|
||
"\n",
|
||
"c. Recurrent Neural Networks\n",
|
||
"\n",
|
||
"* Decisions trees and ensemble methods:\n",
|
||
"\n",
|
||
"a. Decision trees\n",
|
||
"\n",
|
||
"b. Bagging and voting\n",
|
||
"\n",
|
||
"c. Random forests\n",
|
||
"\n",
|
||
"d. Boosting and gradient boosting\n",
|
||
"\n",
|
||
"* Not discussed this year: Support vector machines\n",
|
||
"\n",
|
||
"a. Binary classification and multiclass classification\n",
|
||
"\n",
|
||
"b. Kernel methods\n",
|
||
"\n",
|
||
"c. Regression"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e70a00a",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Learning outcomes and overarching aims of this course\n",
|
||
"\n",
|
||
"The course introduces a variety of central algorithms and methods\n",
|
||
"essential for studies of data analysis and machine learning. The\n",
|
||
"course is project based and through the various projects, normally\n",
|
||
"three, you will be exposed to fundamental research problems\n",
|
||
"in these fields, with the aim to reproduce state of the art scientific\n",
|
||
"results. The students will learn to develop and structure large codes\n",
|
||
"for studying these systems, get acquainted with computing facilities\n",
|
||
"and learn to handle large scientific projects. A good scientific and\n",
|
||
"ethical conduct is emphasized throughout the course. \n",
|
||
"\n",
|
||
"* Understand linear methods for regression and classification;\n",
|
||
"\n",
|
||
"* Learn about neural network;\n",
|
||
"\n",
|
||
"* Learn about bagging, boosting and trees\n",
|
||
"<!-- * Support vector machines -->\n",
|
||
"\n",
|
||
"* Learn about basic data analysis;\n",
|
||
"\n",
|
||
"* Be capable of extending the acquired knowledge to other systems and cases;\n",
|
||
"\n",
|
||
"* Have an understanding of central algorithms used in data analysis and machine learning;\n",
|
||
"\n",
|
||
"* Work on numerical projects to illustrate the theory. The projects play a central role."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "498439cc",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Perspective on Machine Learning\n",
|
||
"\n",
|
||
"1. Rapidly emerging application area\n",
|
||
"\n",
|
||
"2. Experiment AND theory are evolving in many many fields. \n",
|
||
"\n",
|
||
"3. Requires education/retraining for more widespread adoption\n",
|
||
"\n",
|
||
"4. A lot of “word-of-mouth” development methods\n",
|
||
"\n",
|
||
"Huge amounts of data sets require automation, classical analysis tools often inadequate. \n",
|
||
"High energy physics hit this wall in the 90’s.\n",
|
||
"In 2009 single top quark production was determined via [Boosted decision trees, Bayesian\n",
|
||
"Neural Networks, etc.](https://arxiv.org/pdf/0903.0850.pdf)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5aefcdd5",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Machine Learning Research\n",
|
||
"\n",
|
||
"Where to find recent results:\n",
|
||
"1. Conference proceedings, arXiv and blog posts!\n",
|
||
"\n",
|
||
"2. **NIPS**: [Neural Information Processing Systems](https://papers.nips.cc)\n",
|
||
"\n",
|
||
"3. **ICLR**: [International Conference on Learning Representations](https://openreview.net/group?id=ICLR.cc/2018/Conference#accepted-oral-papers)\n",
|
||
"\n",
|
||
"4. **ICML**: International Conference on Machine Learning\n",
|
||
"\n",
|
||
"5. [Journal of Machine Learning Research](http://www.jmlr.org/papers/v19/) \n",
|
||
"\n",
|
||
"6. [Follow ML on ArXiv](https://arxiv.org/list/cs.LG/recent)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e5724f99",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Starting your Machine Learning Project\n",
|
||
"\n",
|
||
"1. Identify problem type: classification, regression\n",
|
||
"\n",
|
||
"2. Consider your data carefully\n",
|
||
"\n",
|
||
"3. Choose a simple model that fits 1 and 2\n",
|
||
"\n",
|
||
"4. Consider your data carefully again! Think of data representation more carefully.\n",
|
||
"\n",
|
||
"5. Based on your results, feedback loop to earliest possible point"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4b014afb",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Choose a Model and Algorithm\n",
|
||
"\n",
|
||
"* Supervised?\n",
|
||
"\n",
|
||
"* Start with the simplest model that fits your problem\n",
|
||
"\n",
|
||
"* Start with minimal processing of data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "86d5bf4b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Preparing Your Data\n",
|
||
"\n",
|
||
"* Shuffle your data\n",
|
||
"\n",
|
||
"* Mean center your data\n",
|
||
"\n",
|
||
" * Why?\n",
|
||
"\n",
|
||
"* Normalize the variance\n",
|
||
"\n",
|
||
" * Why?\n",
|
||
"\n",
|
||
"* **Whitening**\n",
|
||
"\n",
|
||
" * Decorrelates data\n",
|
||
"\n",
|
||
" * Can be hit or miss\n",
|
||
"\n",
|
||
" * When to do train/test split?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6fd99cf9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Which activation and weights to choose in neural networks\n",
|
||
"\n",
|
||
"* RELU? ELU? GELU? etc\n",
|
||
"\n",
|
||
"* Sigmoid or Tanh?\n",
|
||
"\n",
|
||
"* Set all weights to 0? Terrible idea\n",
|
||
"\n",
|
||
"* Set all weights to random values? Small random values"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "40a95915",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Optimization Methods and Hyperparameters\n",
|
||
"* Stochastic gradient descent\n",
|
||
"\n",
|
||
" * Stochastic gradient descent + momentum\n",
|
||
"\n",
|
||
"* State-of-the-art approaches:\n",
|
||
"\n",
|
||
"a. RMSProp\n",
|
||
"\n",
|
||
"b. Adam\n",
|
||
"\n",
|
||
"c. and more\n",
|
||
"\n",
|
||
"Which regularization and hyperparameters? $L_1$ or $L_2$, soft\n",
|
||
"classifiers, depths of trees and many other. Need to explore a large\n",
|
||
"set of hyperparameters and regularization methods."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "de9b4ef9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Resampling\n",
|
||
"\n",
|
||
"When do we resample?\n",
|
||
"\n",
|
||
"1. [Bootstrap](https://www.cambridge.org/core/books/bootstrap-methods-and-their-application/ED2FD043579F27952363566DC09CBD6A)\n",
|
||
"\n",
|
||
"2. [Cross-validation](https://www.youtube.com/watch?v=fSytzGwwBVw&ab_channel=StatQuestwithJoshStarmer)\n",
|
||
"\n",
|
||
"3. Jackknife and many other"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "09e09e8e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Other courses on Data science and Machine Learning at UiO\n",
|
||
"\n",
|
||
"1. [FYS5429 – Advanced machine learning and data analysis for the physical sciences](https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html)\n",
|
||
"\n",
|
||
"2. [IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning](https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html). Introductory course in machine learning and AI\n",
|
||
"\n",
|
||
"3. [STK-INF3000/4000 Selected Topics in Data Science](http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html). The course provides insight into selected contemporary relevant topics within Data Science. \n",
|
||
"\n",
|
||
"4. [IN4080 Natural Language Processing](https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html). Probabilistic and machine learning techniques applied to natural language processing. \n",
|
||
"\n",
|
||
"5. [STK-IN4300 – Statistical learning methods in Data Science](https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html). An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.\n",
|
||
"\n",
|
||
"6. [IN-STK5000 Responsible Data Science](https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html). Methods for adaptive collection and processing of data based on machine learning techniques. \n",
|
||
"\n",
|
||
"7. [IN4310 – Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN4310/index.html). An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.\n",
|
||
"\n",
|
||
"8. [IN5310 – Advanced Deep Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5310/index.html)\n",
|
||
"\n",
|
||
"9. [IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems](https://www.uio.no/studier/emner/matnat/ifi/IN5490/index.html)\n",
|
||
"\n",
|
||
"10. [TEK5040 – Deep learning for autonomous systems](https://www.uio.no/studier/emner/matnat/its/TEK5040/). The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4048b90f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Additional courses of interest\n",
|
||
"\n",
|
||
"1. [STK4051 Computational Statistics](https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html)\n",
|
||
"\n",
|
||
"2. [STK4021 Applied Bayesian Analysis and Numerical Methods](https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d62bdef5",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## What's the future like?\n",
|
||
"\n",
|
||
"Based on multi-layer nonlinear neural networks, deep learning can\n",
|
||
"learn directly from raw data, automatically extract and abstract\n",
|
||
"features from layer to layer, and then achieve the goal of regression,\n",
|
||
"classification, or ranking. Deep learning has made breakthroughs in\n",
|
||
"computer vision, speech processing and natural language, and reached\n",
|
||
"or even surpassed human level. The success of deep learning is mainly\n",
|
||
"due to the three factors: big data, big model, and big computing.\n",
|
||
"\n",
|
||
"In the past few decades, many different architectures of deep neural\n",
|
||
"networks have been proposed, such as\n",
|
||
"1. Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;\n",
|
||
"\n",
|
||
"2. Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;\n",
|
||
"\n",
|
||
"3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7110e8f8",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Types of Machine Learning, a repetition\n",
|
||
"\n",
|
||
"The approaches to machine learning are many, but are often split into two main categories. \n",
|
||
"In *supervised learning* we know the answer to a problem,\n",
|
||
"and let the computer deduce the logic behind it. On the other hand, *unsupervised learning*\n",
|
||
"is a method for finding patterns and relationship in data sets without any prior knowledge of the system.\n",
|
||
"Some authours also operate with a third category, namely *reinforcement learning*. This is a paradigm \n",
|
||
"of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, \n",
|
||
"solely from rewards and punishment.\n",
|
||
"\n",
|
||
"Another way to categorize machine learning tasks is to consider the desired output of a system.\n",
|
||
"Some of the most common tasks are:\n",
|
||
"\n",
|
||
" * Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.\n",
|
||
"\n",
|
||
" * Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.\n",
|
||
"\n",
|
||
" * Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.\n",
|
||
"\n",
|
||
" * Other unsupervised learning algortihms like **Boltzmann machines**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "49da7ac1",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Why Boltzmann machines?\n",
|
||
"\n",
|
||
"What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. \n",
|
||
"One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics.\n",
|
||
"\n",
|
||
"The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. \n",
|
||
"\n",
|
||
"Furthermore, they have been used to solve complicated [quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models](https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.91.045002)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b7ddf675",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Boltzmann Machines\n",
|
||
"\n",
|
||
"Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? \n",
|
||
"\n",
|
||
"* Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.\n",
|
||
"\n",
|
||
"* A generative model can learn to represent and sample from a probability distribution. The core idea is to learn a parametric model of the probability distribution from which the training data was drawn. As an example\n",
|
||
"\n",
|
||
"a. A model for images could learn to draw new examples of cats and dogs, given a training dataset of images of cats and dogs.\n",
|
||
"\n",
|
||
"b. Generate a sample of an ordered or disordered phase, having been given samples of such phases.\n",
|
||
"\n",
|
||
"c. Model the trial function for [Monte Carlo calculations](https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.91.045002)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4f93bfd8",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Some similarities and differences from DNNs\n",
|
||
"\n",
|
||
"1. Both use gradient-descent based learning procedures for minimizing cost functions\n",
|
||
"\n",
|
||
"2. Energy based models don't use backpropagation and automatic differentiation for computing gradients, instead turning to Markov Chain Monte Carlo methods.\n",
|
||
"\n",
|
||
"3. DNNs often have several hidden layers. A restricted Boltzmann machine has only one hidden layer, however several RBMs can be stacked to make up Deep Belief Networks, of which they constitute the building blocks.\n",
|
||
"\n",
|
||
"History: The RBM was developed by amongst others [Geoffrey Hinton](https://en.wikipedia.org/wiki/Geoffrey_Hinton), called by some the \"Godfather of Deep Learning\", working with the University of Toronto and Google."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "553b0708",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Boltzmann machines (BM)\n",
|
||
"\n",
|
||
"A BM is what we would call an undirected probabilistic graphical model\n",
|
||
"with stochastic continuous or discrete units.\n",
|
||
"\n",
|
||
"It is interpreted as a stochastic recurrent neural network where the\n",
|
||
"state of each unit(neurons/nodes) depends on the units it is connected\n",
|
||
"to. The weights in the network represent thus the strength of the\n",
|
||
"interaction between various units/nodes.\n",
|
||
"\n",
|
||
"It turns into a Hopfield network if we choose deterministic rather\n",
|
||
"than stochastic units. In contrast to a Hopfield network, a BM is a\n",
|
||
"so-called generative model. It allows us to generate new samples from\n",
|
||
"the learned distribution."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c82a9f73",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## A standard BM setup\n",
|
||
"\n",
|
||
"A standard BM network is divided into a set of observable and visible units $\\hat{x}$ and a set of unknown hidden units/nodes $\\hat{h}$.\n",
|
||
"\n",
|
||
"Additionally there can be bias nodes for the hidden and visible layers. These biases are normally set to $1$.\n",
|
||
"\n",
|
||
"BMs are stackable, meaning they cwe can train a BM which serves as input to another BM. We can construct deep networks for learning complex PDFs. The layers can be trained one after another, a feature which makes them popular in deep learning\n",
|
||
"\n",
|
||
"However, they are often hard to train. This leads to the introduction of so-called restricted BMs, or RBMS.\n",
|
||
"Here we take away all lateral connections between nodes in the visible layer as well as connections between nodes in the hidden layer. The network is illustrated in the figure below."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5791b95e",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## The structure of the RBM network\n",
|
||
"\n",
|
||
"<!-- dom:FIGURE: [figures/RBM.png, width=800 frac=1.0] -->\n",
|
||
"<!-- begin figure -->\n",
|
||
"\n",
|
||
"<img src=\"figures/RBM.png\" width=\"800\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
||
"<!-- end figure -->"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2681e655",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## The network\n",
|
||
"\n",
|
||
"**The network layers**:\n",
|
||
"1. A function $\\mathbf{x}$ that represents the visible layer, a vector of $M$ elements (nodes). This layer represents both what the RBM might be given as training input, and what we want it to be able to reconstruct. This might for example be given by the pixels of an image or coefficients representing speech, or the coordinates of a quantum mechanical state function.\n",
|
||
"\n",
|
||
"2. The function $\\mathbf{h}$ represents the hidden, or latent, layer. A vector of $N$ elements (nodes). Also called \"feature detectors\"."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1764e858",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Goals\n",
|
||
"\n",
|
||
"The goal of the hidden layer is to increase the model's expressive\n",
|
||
"power. We encode complex interactions between visible variables by\n",
|
||
"introducing additional, hidden variables that interact with visible\n",
|
||
"degrees of freedom in a simple manner, yet still reproduce the complex\n",
|
||
"correlations between visible degrees in the data once marginalized\n",
|
||
"over (integrated out).\n",
|
||
"\n",
|
||
"**The network parameters, to be optimized/learned**:\n",
|
||
"1. $\\mathbf{a}$ represents the visible bias, a vector of same length as $\\mathbf{x}$.\n",
|
||
"\n",
|
||
"2. $\\mathbf{b}$ represents the hidden bias, a vector of same lenght as $\\mathbf{h}$.\n",
|
||
"\n",
|
||
"3. $W$ represents the interaction weights, a matrix of size $M\\times N$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "435da59f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Joint distribution\n",
|
||
"\n",
|
||
"The restricted Boltzmann machine is described by a Boltzmann distribution"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c7959e4f",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto1\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
"\tP_{rbm}(\\mathbf{x},\\mathbf{h}) = \\frac{1}{Z} e^{-\\frac{1}{T_0}E(\\mathbf{x},\\mathbf{h})},\n",
|
||
"\\label{_auto1} \\tag{1}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fa48be28",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where $Z$ is the normalization constant or partition function, defined as"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "02027f10",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto2\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
"\tZ = \\int \\int e^{-\\frac{1}{T_0}E(\\mathbf{x},\\mathbf{h})} d\\mathbf{x} d\\mathbf{h}.\n",
|
||
"\\label{_auto2} \\tag{2}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7247c2cd",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"It is common to ignore $T_0$ by setting it to one."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0f51c953",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Network Elements, the energy function\n",
|
||
"\n",
|
||
"The function $E(\\mathbf{x},\\mathbf{h})$ gives the **energy** of a\n",
|
||
"configuration (pair of vectors) $(\\mathbf{x}, \\mathbf{h})$. The lower\n",
|
||
"the energy of a configuration, the higher the probability of it. This\n",
|
||
"function also depends on the parameters $\\mathbf{a}$, $\\mathbf{b}$ and\n",
|
||
"$W$. Thus, when we adjust them during the learning procedure, we are\n",
|
||
"adjusting the energy function to best fit our problem.\n",
|
||
"\n",
|
||
"An expression for the energy function is"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3ed82ee8",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"$$\n",
|
||
"E(\\hat{x},\\hat{h}) = -\\sum_{ia}^{NA}b_i^a \\alpha_i^a(x_i)-\\sum_{jd}^{MD}c_j^d \\beta_j^d(h_j)-\\sum_{ijad}^{NAMD}b_i^a \\alpha_i^a(x_i)c_j^d \\beta_j^d(h_j)w_{ij}^{ad}.\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f95ef8c8",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"Here $\\beta_j^d(h_j)$ and $\\alpha_i^a(x_j)$ are so-called transfer functions that map a given input value to a desired feature value. The labels $a$ and $d$ denote that there can be multiple transfer functions per variable. The first sum depends only on the visible units. The second on the hidden ones. **Note** that there is no connection between nodes in a layer.\n",
|
||
"\n",
|
||
"The quantities $b$ and $c$ can be interpreted as the visible and hidden biases, respectively.\n",
|
||
"\n",
|
||
"The connection between the nodes in the two layers is given by the weights $w_{ij}$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "36be46d9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Defining different types of RBMs\n",
|
||
"There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function $E(\\mathbf{x},\\mathbf{h})$. \n",
|
||
"\n",
|
||
"**Binary-Binary RBM:**\n",
|
||
"\n",
|
||
"RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "374b4069",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto3\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
"\tE(\\mathbf{x}, \\mathbf{h}) = - \\sum_i^M x_i a_i- \\sum_j^N b_j h_j - \\sum_{i,j}^{M,N} x_i w_{ij} h_j,\n",
|
||
"\\label{_auto3} \\tag{3}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5676ade3",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"where the binary values taken on by the nodes are most commonly 0 and 1.\n",
|
||
"\n",
|
||
"**Gaussian-Binary RBM:**\n",
|
||
"\n",
|
||
"Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5dc3dfdc",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"<!-- Equation labels as ordinary links -->\n",
|
||
"<div id=\"_auto4\"></div>\n",
|
||
"\n",
|
||
"$$\n",
|
||
"\\begin{equation}\n",
|
||
"\tE(\\mathbf{x}, \\mathbf{h}) = \\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma_i^2} - \\sum_j^N b_j h_j - \\sum_{i,j}^{M,N} \\frac{x_i w_{ij} h_j}{\\sigma_i^2}. \n",
|
||
"\\label{_auto4} \\tag{4}\n",
|
||
"\\end{equation}\n",
|
||
"$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7c26c421",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## More about RBMs\n",
|
||
"1. Useful when we model continuous data (i.e., we wish $\\mathbf{x}$ to be continuous)\n",
|
||
"\n",
|
||
"2. Requires a smaller learning rate, since there's no upper bound to the value a component might take in the reconstruction\n",
|
||
"\n",
|
||
"Other types of units include:\n",
|
||
"1. Softmax and multinomial units\n",
|
||
"\n",
|
||
"2. Gaussian visible and hidden units\n",
|
||
"\n",
|
||
"3. Binomial units\n",
|
||
"\n",
|
||
"4. Rectified linear units\n",
|
||
"\n",
|
||
"To read more, see [Lectures on Boltzmann machines in Physics](https://github.com/CompPhysics/ComputationalPhysics2/blob/gh-pages/doc/pub/notebook2/ipynb/notebook2.ipynb)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "73947428",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Autoencoders: Overarching view\n",
|
||
"\n",
|
||
"Autoencoders are artificial neural networks capable of learning\n",
|
||
"efficient representations of the input data (these representations are called codings) without\n",
|
||
"any supervision (i.e., the training set is unlabeled). These codings\n",
|
||
"typically have a much lower dimensionality than the input data, making\n",
|
||
"autoencoders useful for dimensionality reduction. \n",
|
||
"\n",
|
||
"More importantly, autoencoders act as powerful feature detectors, and\n",
|
||
"they can be used for unsupervised pretraining of deep neural networks.\n",
|
||
"\n",
|
||
"Lastly, they are capable of randomly generating new data that looks\n",
|
||
"very similar to the training data; this is called a generative\n",
|
||
"model. For example, you could train an autoencoder on pictures of\n",
|
||
"faces, and it would then be able to generate new faces. Surprisingly,\n",
|
||
"autoencoders work by simply learning to copy their inputs to their\n",
|
||
"outputs. This may sound like a trivial task, but we will see that\n",
|
||
"constraining the network in various ways can make it rather\n",
|
||
"difficult. For example, you can limit the size of the internal\n",
|
||
"representation, or you can add noise to the inputs and train the\n",
|
||
"network to recover the original inputs. These constraints prevent the\n",
|
||
"autoencoder from trivially copying the inputs directly to the outputs,\n",
|
||
"which forces it to learn efficient ways of representing the data. In\n",
|
||
"short, the codings are byproducts of the autoencoder’s attempt to\n",
|
||
"learn the identity function under some constraints.\n",
|
||
"\n",
|
||
"[Video on autoencoders](https://www.coursera.org/lecture/building-deep-learning-models-with-tensorflow/autoencoders-1U4L3)\n",
|
||
"\n",
|
||
"See also A. Geron's textbook, chapter 15."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "69763d57",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Bayesian Machine Learning\n",
|
||
"\n",
|
||
"This is an important topic if we aim at extracting a probability\n",
|
||
"distribution. This gives us also a confidence interval and error\n",
|
||
"estimates.\n",
|
||
"\n",
|
||
"Bayesian machine learning allows us to encode our prior beliefs about\n",
|
||
"what those models should look like, independent of what the data tells\n",
|
||
"us. This is especially useful when we don’t have a ton of data to\n",
|
||
"confidently learn our model.\n",
|
||
"\n",
|
||
"[Video on Bayesian deep learning](https://www.youtube.com/watch?v=E1qhGw8QxqY&ab_channel=AndrewGordonWilson)\n",
|
||
"\n",
|
||
"See also the [slides here](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Articles/lec03.pdf)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3ee70227",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Reinforcement Learning\n",
|
||
"\n",
|
||
"Reinforcement Learning (RL) is one of the most exciting fields of\n",
|
||
"Machine Learning today, and also one of the oldest. It has been around\n",
|
||
"since the 1950s, producing many interesting applications over the\n",
|
||
"years.\n",
|
||
"\n",
|
||
"It studies\n",
|
||
"how agents take actions based on trial and error, so as to maximize\n",
|
||
"some notion of cumulative reward in a dynamic system or\n",
|
||
"environment. Due to its generality, the problem has also been studied\n",
|
||
"in many other disciplines, such as game theory, control theory,\n",
|
||
"operations research, information theory, multi-agent systems, swarm\n",
|
||
"intelligence, statistics, and genetic algorithms.\n",
|
||
"\n",
|
||
"In March 2016, AlphaGo, a computer program that plays the board game\n",
|
||
"Go, beat Lee Sedol in a five-game match. This was the first time a\n",
|
||
"computer Go program had beaten a 9-dan (highest rank) professional\n",
|
||
"without handicaps. AlphaGo is based on deep convolutional neural\n",
|
||
"networks and reinforcement learning. AlphaGo’s victory was a major\n",
|
||
"milestone in artificial intelligence and it has also made\n",
|
||
"reinforcement learning a hot research area in the field of machine\n",
|
||
"learning.\n",
|
||
"\n",
|
||
"[Lecture on Reinforcement Learning](https://www.youtube.com/watch?v=FgzM3zpZ55o&ab_channel=stanfordonline).\n",
|
||
"\n",
|
||
"See also A. Geron's textbook, chapter 16."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c8fe760",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Transfer learning\n",
|
||
"\n",
|
||
"The goal of transfer learning is to transfer the model or knowledge\n",
|
||
"obtained from a source task to the target task, in order to resolve\n",
|
||
"the issues of insufficient training data in the target task. The\n",
|
||
"rationality of doing so lies in that usually the source and target\n",
|
||
"tasks have inter-correlations, and therefore either the features,\n",
|
||
"samples, or models in the source task might provide useful information\n",
|
||
"for us to better solve the target task. Transfer learning is a hot\n",
|
||
"research topic in recent years, with many problems still waiting to be studied.\n",
|
||
"\n",
|
||
"[Lecture on transfer learning](https://www.ias.edu/video/machinelearning/2020/0331-SamoryKpotufe)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "82d92000",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Adversarial learning\n",
|
||
"\n",
|
||
"The conventional deep generative model has a potential problem: the\n",
|
||
"model tends to generate extreme instances to maximize the\n",
|
||
"probabilistic likelihood, which will hurt its performance. Adversarial\n",
|
||
"learning utilizes the adversarial behaviors (e.g., generating\n",
|
||
"adversarial instances or training an adversarial model) to enhance the\n",
|
||
"robustness of the model and improve the quality of the generated\n",
|
||
"data. In recent years, one of the most promising unsupervised learning\n",
|
||
"technologies, generative adversarial networks (GAN), has already been\n",
|
||
"successfully applied to image, speech, and text.\n",
|
||
"\n",
|
||
"[Lecture on adversial learning](https://www.youtube.com/watch?v=CIfsB_EYsVI&ab_channel=StanfordUniversitySchoolofEngineering)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "81af1340",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Dual learning\n",
|
||
"\n",
|
||
"Dual learning is a new learning paradigm, the basic idea of which is\n",
|
||
"to use the primal-dual structure between machine learning tasks to\n",
|
||
"obtain effective feedback/regularization, and guide and strengthen the\n",
|
||
"learning process, thus reducing the requirement of large-scale labeled\n",
|
||
"data for deep learning. The idea of dual learning has been applied to\n",
|
||
"many problems in machine learning, including machine translation,\n",
|
||
"image style conversion, question answering and generation, image\n",
|
||
"classification and generation, text classification and generation,\n",
|
||
"image-to-text, and text-to-image."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9ccf4491",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Distributed machine learning\n",
|
||
"\n",
|
||
"Distributed computation will speed up machine learning algorithms,\n",
|
||
"significantly improve their efficiency, and thus enlarge their\n",
|
||
"application. When distributed meets machine learning, more than just\n",
|
||
"implementing the machine learning algorithms in parallel is required."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d561f7fa",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Meta learning\n",
|
||
"\n",
|
||
"Meta learning is an emerging research direction in machine\n",
|
||
"learning. Roughly speaking, meta learning concerns learning how to\n",
|
||
"learn, and focuses on the understanding and adaptation of the learning\n",
|
||
"itself, instead of just completing a specific learning task. That is,\n",
|
||
"a meta learner needs to be able to evaluate its own learning methods\n",
|
||
"and adjust its own learning methods according to specific learning\n",
|
||
"tasks."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2f59b95d",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## The Challenges Facing Machine Learning\n",
|
||
"\n",
|
||
"While there has been much progress in machine learning, there are also challenges.\n",
|
||
"\n",
|
||
"For example, the mainstream machine learning technologies are\n",
|
||
"black-box approaches, making us concerned about their potential\n",
|
||
"risks. To tackle this challenge, we may want to make machine learning\n",
|
||
"more explainable and controllable. As another example, the\n",
|
||
"computational complexity of machine learning algorithms is usually\n",
|
||
"very high and we may want to invent lightweight algorithms or\n",
|
||
"implementations. Furthermore, in many domains such as physics,\n",
|
||
"chemistry, biology, and social sciences, people usually seek elegantly\n",
|
||
"simple equations (e.g., the Schrödinger equation) to uncover the\n",
|
||
"underlying laws behind various phenomena. In the field of machine\n",
|
||
"learning, can we reveal simple laws instead of designing more complex\n",
|
||
"models for data fitting? Although there are many challenges, we are\n",
|
||
"still very optimistic about the future of machine learning. As we look\n",
|
||
"forward to the future, here are what we think the research hotspots in\n",
|
||
"the next ten years will be.\n",
|
||
"\n",
|
||
"See the article on [Discovery of Physics From Data: Universal Laws and Discrepancies](https://www.frontiersin.org/articles/10.3389/frai.2020.00025/full)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "89648aa7",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Explainable machine learning\n",
|
||
"\n",
|
||
"Machine learning, especially deep learning, evolves rapidly. The\n",
|
||
"ability gap between machine and human on many complex cognitive tasks\n",
|
||
"becomes narrower and narrower. However, we are still in the very early\n",
|
||
"stage in terms of explaining why those effective models work and how\n",
|
||
"they work.\n",
|
||
"\n",
|
||
"**What is missing: the gap between correlation and causation**. Standard Machine Learning is based on what e have called a frequentist approach. \n",
|
||
"\n",
|
||
"Most\n",
|
||
"machine learning techniques, especially the statistical ones, depend\n",
|
||
"highly on correlations in data sets to make predictions and analyses. In\n",
|
||
"contrast, rational humans tend to reply on clear and trustworthy\n",
|
||
"causality relations obtained via logical reasoning on real and clear\n",
|
||
"facts. It is one of the core goals of explainable machine learning to\n",
|
||
"transition from solving problems by data correlation to solving\n",
|
||
"problems by logical reasoning.\n",
|
||
"\n",
|
||
"**Bayesian Machine Learning is one of the exciting research directions in this field**."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1bd0711b",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Quantum machine learning\n",
|
||
"\n",
|
||
"Quantum machine learning is an emerging interdisciplinary research\n",
|
||
"area at the intersection of quantum computing and machine learning.\n",
|
||
"\n",
|
||
"Quantum computers use effects such as quantum coherence and quantum\n",
|
||
"entanglement to process information, which is fundamentally different\n",
|
||
"from classical computers. Quantum algorithms have surpassed the best\n",
|
||
"classical algorithms in several problems (e.g., searching for an\n",
|
||
"unsorted database, inverting a sparse matrix), which we call quantum\n",
|
||
"acceleration.\n",
|
||
"\n",
|
||
"When quantum computing meets machine learning, it can be a mutually\n",
|
||
"beneficial and reinforcing process, as it allows us to take advantage\n",
|
||
"of quantum computing to improve the performance of classical machine\n",
|
||
"learning algorithms. In addition, we can also use the machine learning\n",
|
||
"algorithms (on classic computers) to analyze and improve quantum\n",
|
||
"computing systems.\n",
|
||
"\n",
|
||
"[Lecture on Quantum ML](https://www.youtube.com/watch?v=Xh9pUu3-WxM&ab_channel=InstituteforPure%26AppliedMathematics%28IPAM%29).\n",
|
||
"\n",
|
||
"[Read interview with Maria Schuld on her work on Quantum Machine Learning](https://physics.aps.org/articles/v13/179?utm_campaign=weekly&utm_medium=email&utm_source=emailalert). See also [her recent textbook](https://www.springer.com/gp/book/9783319964232)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2e71e339",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Quantum machine learning algorithms based on linear algebra\n",
|
||
"\n",
|
||
"Many quantum machine learning algorithms are based on variants of\n",
|
||
"quantum algorithms for solving linear equations, which can efficiently\n",
|
||
"solve N-variable linear equations with complexity of O(log2 N) under\n",
|
||
"certain conditions. The quantum matrix inversion algorithm can\n",
|
||
"accelerate many machine learning methods, such as least square linear\n",
|
||
"regression, least square version of support vector machine, Gaussian\n",
|
||
"process, and more. The training of these algorithms can be simplified\n",
|
||
"to solve linear equations. The key bottleneck of this type of quantum\n",
|
||
"machine learning algorithms is data input—that is, how to initialize\n",
|
||
"the quantum system with the entire data set. Although efficient\n",
|
||
"data-input algorithms exist for certain situations, how to efficiently\n",
|
||
"input data into a quantum system is as yet unknown for most cases."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3f1e33e9",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Quantum reinforcement learning\n",
|
||
"\n",
|
||
"In quantum reinforcement learning, a quantum agent interacts with the\n",
|
||
"classical environment to obtain rewards from the environment, so as to\n",
|
||
"adjust and improve its behavioral strategies. In some cases, it\n",
|
||
"achieves quantum acceleration by the quantum processing capabilities\n",
|
||
"of the agent or the possibility of exploring the environment through\n",
|
||
"quantum superposition. Such algorithms have been proposed in\n",
|
||
"superconducting circuits and systems of trapped ions."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0b2bc8fa",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Quantum deep learning\n",
|
||
"\n",
|
||
"Dedicated quantum information processors, such as quantum annealers\n",
|
||
"and programmable photonic circuits, are well suited for building deep\n",
|
||
"quantum networks. The simplest deep quantum network is the Boltzmann\n",
|
||
"machine. The classical Boltzmann machine consists of bits with tunable\n",
|
||
"interactions and is trained by adjusting the interaction of these bits\n",
|
||
"so that the distribution of its expression conforms to the statistics\n",
|
||
"of the data. To quantize the Boltzmann machine, the neural network can\n",
|
||
"simply be represented as a set of interacting quantum spins that\n",
|
||
"correspond to an adjustable Ising model. Then, by initializing the\n",
|
||
"input neurons in the Boltzmann machine to a fixed state and allowing\n",
|
||
"the system to heat up, we can read out the output qubits to get the\n",
|
||
"result."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3fad774c",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Social machine learning\n",
|
||
"\n",
|
||
"Machine learning aims to imitate how humans\n",
|
||
"learn. While we have developed successful machine learning algorithms,\n",
|
||
"until now we have ignored one important fact: humans are social. Each\n",
|
||
"of us is one part of the total society and it is difficult for us to\n",
|
||
"live, learn, and improve ourselves, alone and isolated. Therefore, we\n",
|
||
"should design machines with social properties. Can we let machines\n",
|
||
"evolve by imitating human society so as to achieve more effective,\n",
|
||
"intelligent, interpretable “social machine learning”?\n",
|
||
"\n",
|
||
"And much more."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f418cc25",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## The last words?\n",
|
||
"\n",
|
||
"Early computer scientist Alan Kay said, **The best way to predict the\n",
|
||
"future is to create it**. Therefore, all machine learning\n",
|
||
"practitioners, whether scholars or engineers, professors or students,\n",
|
||
"need to work together to advance these important research\n",
|
||
"topics. Together, we will not just predict the future, but create it."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5b323429",
|
||
"metadata": {
|
||
"editable": true
|
||
},
|
||
"source": [
|
||
"## Best wishes to you all and thanks so much for your heroic efforts this semester\n",
|
||
"\n",
|
||
"<!-- dom:FIGURE: [figures/Nebbdyr2.png, width=500 frac=0.6] -->\n",
|
||
"<!-- begin figure -->\n",
|
||
"\n",
|
||
"<img src=\"figures/Nebbdyr2.png\" width=\"500\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
||
"<!-- end figure -->"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.9.15"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
} |