{ "cells": [ { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "# Lecture 17: Ensemble learning and random forests" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "skip" }, "tags": [] }, "source": [ "![](https://www.tensorflow.org/images/colab_logo_32px.png)\n", "[Run in colab](https://colab.research.google.com/drive/1SNSqzKBuOnHfK_G_agJQtmE8y3WM2EhI)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:28.589474Z", "iopub.status.busy": "2025-02-27T23:21:28.589245Z", "iopub.status.idle": "2025-02-27T23:21:28.595460Z", "shell.execute_reply": "2025-02-27T23:21:28.594870Z" }, "slideshow": { "slide_type": "skip" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Last executed: 2025-02-27 23:21:28\n" ] } ], "source": [ "import datetime\n", "now = datetime.datetime.now()\n", "print(\"Last executed: \" + now.strftime(\"%Y-%m-%d %H:%M:%S\"))" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Ensemble learning overview\n", "\n", "- Leverage *wisdom of the crowd*.\n", "- The average from many predictors (or people) may be more accurate that the result from any single given predictor.\n", "- Group of predictors called an *ensemble*.\n", "- Even if individual predictors/classifiers are *weak* (only slightly better than random), an ensemble can be *strong* (high accuracy)." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Two broad approaches:\n", "- Different predictors.\n", "- Same predictor, different training sets.\n", "\n", "An example of ensemble learning is *Random Forests* where *Decision Trees* are trained on random _*subsets*_ of the training data and then for each sample the assigned class is the one that gets the most *votes* from the ensemble." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Voting" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Consider a number of classifiers.\n", "\n", "\"data-layout\"\n", "\n", "[Source: Geron]" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Majority wins (hard voting) \n", "\n", "\"data-layout\"\n", "\n", "[Source: Geron]\n", "\n", "*Majorty wins* is often called *hard voting*.\n", "\n", "Also _soft_ voting (see upcoming slides and example)." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Example" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:28.628304Z", "iopub.status.busy": "2025-02-27T23:21:28.628127Z", "iopub.status.idle": "2025-02-27T23:21:29.475994Z", "shell.execute_reply": "2025-02-27T23:21:29.475366Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "# Build a voting classifier in Scikit using three weaker classifiers\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", "\n", "# Use moons dataset\n", "X, y = make_moons(n_samples=500, noise=0.30, random_state=42) #X(features), y(classifications)\n", "\n", "# Split into training and test data \n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) \n", "\n", "# Load three different classification algorithms, and initialise \n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "log_clf = LogisticRegression(random_state=42)\n", "rnd_clf = RandomForestClassifier(random_state=42)\n", "svm_clf = SVC(probability=True, random_state=42)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:29.478273Z", "iopub.status.busy": "2025-02-27T23:21:29.478018Z", "iopub.status.idle": "2025-02-27T23:21:29.628799Z", "shell.execute_reply": "2025-02-27T23:21:29.628226Z" }, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.ensemble import VotingClassifier\n", "\n", "# Voting classifier \"=\" logistic + random forest + SVC\n", "# Set up the voting classifier\n", "voting_clf = VotingClassifier(\n", " estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n", " voting='soft')\n", "\n", "# Classify using voting classifier (auto uses all the assigned invidiual classifiers)\n", "voting_clf.fit(X_train, y_train);" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:29.630674Z", "iopub.status.busy": "2025-02-27T23:21:29.630486Z", "iopub.status.idle": "2025-02-27T23:21:29.936574Z", "shell.execute_reply": "2025-02-27T23:21:29.936202Z" }, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LogisticRegression 0.864\n", "RandomForestClassifier 0.896\n", "SVC 0.896\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "VotingClassifier 0.92\n" ] } ], "source": [ "# Let's see how each individual classifier did:\n", "from sklearn.metrics import accuracy_score\n", "\n", "for clf in (log_clf, rnd_clf, svm_clf, voting_clf): #loop over classifiers\n", " clf.fit(X_train, y_train) #fit each one individually \n", " y_pred = clf.predict(X_test) #make prediction \n", " print(clf.__class__.__name__, accuracy_score(y_test, y_pred)) #print the score " ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Voting classifier did better than 3 individual ones!" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Hard vs soft voting\n", "\n", "* Hard voting: select class with highest frequency across predictors, without weight taken into account.\n", "* Soft voting: if all classifiers can estimate class probabilities (i.e. they have a `predict_proba()` method), predict overall class probability, averaged over all individual classifiers, and select highest. \n", "\n", "Soft voting is often better than hard voting because it gives more weight to highly confident, but perhaps less frequent, votes." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Bagging and pasting \n", "\n", "Instead of using different predictors, we can use same the predictor but different training sets. " ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "\"data-layout\"" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Two ways to create new training sets: \n", "- When sampling is performed *with replacement*, this method is called *bagging* (short for bootstrap aggregating).\n", "- When sampling is performed *without replacement*, it is called *pasting*.\n", "\n", "A big advantage is that trivially parallisable across nodes/CPU." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Bagging \n", "\n", "Given a standard training set $D$ of size $n$, **bagging** generates $m$ new training sets $D_{i}$, each of size $n'" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))\n", "\n", "plot_decision_boundary(ax1, tree_clf, X, y), ax1.set_title(\"Decision Tree\")\n", "\n", "plot_decision_boundary(ax2, bag_clf, X, y), ax2.set_title(\"Decision Trees with Bagging\");" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Bagging versus pasting\n", "\n", "* Bootstrapping introduces a bit more diversity in the subsets that each predictor is trained on, so bagging ends up with a slightly higher bias than pasting.\n", "* But this also means that predictors end up being less correlated so the ensemble’s variance is reduced.\n", "* Overall, bagging often results in better models, which explains why it is generally preferred.\n", "* However, if you have spare time and CPU power you can use cross-validation to evaluate both bagging and pasting and select the one that works best.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Out-of-bag evaluation\n", "\n", "* With bagging, some instances may be sampled several times for any given predictor, while others may not be sampled at all. \n", "* By default a ```BaggingClassifier``` samples $m$ training instances with replacement (```bootstrap = True```), where $m$ is the size of the training set.\n", "* Only about ~63% of the training instances are sampled on average for any given predictor.\n", "\n", "* The remaining 37% of the training instances that are not sampled are called _out-of-bag (oob) instances_. Note that they are not the same 37% for all predictors.\n", "\n", "Since a predictor never sees the oob instances during training, it can be used to give a clean evaluation on the oob instances, without the need for a validation set." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:31.422340Z", "iopub.status.busy": "2025-02-27T23:21:31.421969Z", "iopub.status.idle": "2025-02-27T23:21:34.657016Z", "shell.execute_reply": "2025-02-27T23:21:34.656250Z" }, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0.8986666666666666" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use oob_score=True in Scikit to do automatic oob evaluation after training.\n", "bag_clf = BaggingClassifier(\n", " DecisionTreeClassifier(),\n", " n_estimators=1000,\n", " bootstrap=True, #using replacement \n", " n_jobs=-1,\n", " oob_score=True\n", ")\n", "bag_clf.fit(X_train, y_train)\n", "bag_clf.oob_score_" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "Should expect about 90% accuracy on the test set. Let's see..." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:34.659228Z", "iopub.status.busy": "2025-02-27T23:21:34.659028Z", "iopub.status.idle": "2025-02-27T23:21:34.984277Z", "shell.execute_reply": "2025-02-27T23:21:34.983579Z" }, "slideshow": { "slide_type": "fragment" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0.88" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import accuracy_score\n", "y_pred = bag_clf.predict(X_test)\n", "accuracy_score(y_test,y_pred)" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Sampling features\n", "\n", "Instead of just sampling training instances, we can also randomly sample the (set of) features themselves." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Random patches\n", "\n", "Sampling *both* instances and features is known as **random patches** method.\n", "\n", "In SciKit-Learn this is done by using the `max_features` and `bootstrap_features=True` key words in `BaggingClassifier`.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Random sub-spaces\n", "\n", "Keeping all training instances (i.e., ```bootstrap = False``` and ```max_samples = 1.0```) but sampling features only (i.e., ```bootstrap_features = True``` and/ or ```max_features``` smaller than 1.0) is called the **random subspaces** method." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Random forests\n", "\n", "A random forest is an ensemble of decision trees.\n", "\n", "We have actually been manually creating these in the previous examples explicitly!\n", "\n", "But we can use built-in SciKit-Learn functionality.\n", "* ```RandomForestClassifier```: specially designed for classification.\n", "* ```RandomForestRegressor```: specially designed for regression.\n", "\n", "Typically trained via bagging, although alternatives can be considered." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train a random forest directly." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2025-02-27T23:21:34.986482Z", "iopub.status.busy": "2025-02-27T23:21:34.986292Z", "iopub.status.idle": "2025-02-27T23:21:35.726270Z", "shell.execute_reply": "2025-02-27T23:21:35.725659Z" } }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16,\n", " n_jobs=-1, random_state=42)\n", "rnd_clf.fit(X_train, y_train)\n", "y_pred_rf = rnd_clf.predict(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Equivalane to a bag of decision trees." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2025-02-27T23:21:35.728677Z", "iopub.status.busy": "2025-02-27T23:21:35.728236Z", "iopub.status.idle": "2025-02-27T23:21:35.733155Z", "shell.execute_reply": "2025-02-27T23:21:35.732576Z" } }, "outputs": [], "source": [ "bag_clf = BaggingClassifier(\n", " DecisionTreeClassifier(max_features=\"sqrt\", max_leaf_nodes=16),\n", " n_estimators=500, n_jobs=-1, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check consistent predictions" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2025-02-27T23:21:35.734992Z", "iopub.status.busy": "2025-02-27T23:21:35.734794Z", "iopub.status.idle": "2025-02-27T23:21:36.552229Z", "shell.execute_reply": "2025-02-27T23:21:36.551813Z" } }, "outputs": [ { "data": { "text/plain": [ "np.True_" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bag_clf.fit(X_train, y_train)\n", "y_pred_bag = bag_clf.predict(X_test)\n", "np.all(y_pred_bag == y_pred_rf) # same predictions" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Extremely randomized trees\n", "\n", "When you are growing a tree in a random forest, at each node only a random subset of the features is considered for splitting (as discussed earlier). \n", "\n", "It is possible to make trees even more random by also using *random thresholds* for each feature rather than searching for the *best possible thresholds* (like regular decision trees do).\n", "\n", "Implemented using `ExtraTreesClassifier`." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:36.554188Z", "iopub.status.busy": "2025-02-27T23:21:36.554005Z", "iopub.status.idle": "2025-02-27T23:21:37.131434Z", "shell.execute_reply": "2025-02-27T23:21:37.130848Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.912\n" ] } ], "source": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "\n", "rnd_clf = ExtraTreesClassifier(\n", " n_estimators=500, \n", " max_leaf_nodes=16, \n", " n_jobs=-1, \n", " random_state=42)\n", "\n", "rnd_clf.fit(X_train, y_train)\n", "y_pred_et = rnd_clf.predict(X_test)\n", "print(accuracy_score(y_test, y_pred_et))" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Feature importance\n", "\n", "Can measure relative feature importance by looking at how much the tree nodes that use that feature reduce impurity on average.\n", "\n", "Specifically, a weighted average is computed, where each node's weight is equal to the number of associated training samples." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "\n", "Can access using ```feature_importances_``` variable.\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:37.133484Z", "iopub.status.busy": "2025-02-27T23:21:37.133307Z", "iopub.status.idle": "2025-02-27T23:21:37.846441Z", "shell.execute_reply": "2025-02-27T23:21:37.845846Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sepal length (cm) = 0.11249225099876375\n", "sepal width (cm) = 0.02311928828251033\n", "petal length (cm) = 0.4410304643639577\n", "petal width (cm) = 0.4233579963547682\n" ] } ], "source": [ "# Rank features by importance in iris data (note switching to Iris from Moons)\n", "\n", "from sklearn.datasets import load_iris\n", "iris = load_iris()\n", "\n", "X=iris[\"data\"]\n", "y=iris[\"target\"]\n", "\n", "rnd_clf = RandomForestClassifier(\n", " n_estimators=500, \n", " n_jobs=-1, \n", " random_state=42)\n", "\n", "rnd_clf.fit(X,y)\n", "\n", "# Print out the importances \n", "for name, importance in zip(iris[\"feature_names\"], rnd_clf.feature_importances_): \n", " print(name, \"=\", importance) " ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Boosting\n", "\n", "General term to mean combining several weak learners into a single strong learner.\n", "\n", "The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. There are many boosting methods available but the most popular are *AdaBoost* (adaptive boosting) and *gradient boosting*. \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### AdaBoost\n", "\n", "Give greater weight to training instances that were underfitted in predecessor.\n", "\n", "\"data-layout\"\n", "\n", "[Source: Geron]" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "-" }, "tags": [] }, "source": [ "AdaBoost pays more attention to training instances that predecessor underfitted, which forces new predictors to concentrate more on the \"hard cases\"." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "fragment" }, "tags": [] }, "source": [ "Disadvantage: results depend on previous classifier (sequential), so algorithm cannot be parallelized. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Gradient boosting \n", "\n", "Just like AdaBoost, Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. \n", "\n", "However, instead of tweaking the instance weights at every iteration like AdaBoost does, gradient boosting tries to fit the new predictor to the *residual error* made by the previous predictor." ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### Example\n", "\n", "Consider an example using decision trees as the base predictors for a regression problem. This is called Gradient Tree Boosting or Gradient Boosted Regression Trees (GBRT).\n", "\n", "First, let's implement this by hand." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:37.848645Z", "iopub.status.busy": "2025-02-27T23:21:37.848463Z", "iopub.status.idle": "2025-02-27T23:21:37.852092Z", "shell.execute_reply": "2025-02-27T23:21:37.851497Z" }, "slideshow": { "slide_type": "-" }, "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.tree import DecisionTreeRegressor\n", "\n", "# Training set: a noisy quadratic function\n", "np.random.seed(42)\n", "X = np.random.rand(100, 1) - 0.5\n", "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2025-02-27T23:21:37.853845Z", "iopub.status.busy": "2025-02-27T23:21:37.853671Z", "iopub.status.idle": "2025-02-27T23:21:37.857535Z", "shell.execute_reply": "2025-02-27T23:21:37.856961Z" }, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# Train Regressor\n", "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", "tree_reg1.fit(X, y);" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2025-02-27T23:21:37.859257Z", "iopub.status.busy": "2025-02-27T23:21:37.859084Z", "iopub.status.idle": "2025-02-27T23:21:37.863265Z", "shell.execute_reply": "2025-02-27T23:21:37.862677Z" } }, "outputs": [], "source": [ "# now train 2nd Regressor using errors made by 1st one.\n", "y2 = y - tree_reg1.predict(X) #residual from the first fit (data-prediction)\n", "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", "tree_reg2.fit(X, y2);" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2025-02-27T23:21:37.865063Z", "iopub.status.busy": "2025-02-27T23:21:37.864868Z", "iopub.status.idle": "2025-02-27T23:21:37.869942Z", "shell.execute_reply": "2025-02-27T23:21:37.869357Z" } }, "outputs": [], "source": [ "# now train 3rd Regressor using errors made by 2nd one.\n", "y3 = y2 - tree_reg2.predict(X)\n", "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", "tree_reg3.fit(X, y3)\n", "\n", "X_new = np.array([[0.8]])\n", "\n", "# now have ensemble w/ three trees.\n", "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:37.871650Z", "iopub.status.busy": "2025-02-27T23:21:37.871479Z", "iopub.status.idle": "2025-02-27T23:21:37.875614Z", "shell.execute_reply": "2025-02-27T23:21:37.875034Z" }, "slideshow": { "slide_type": "skip" }, "tags": [] }, "outputs": [], "source": [ "def plot_predictions(\n", " regressors, X, y, axes,\n", " ax,\n", " label=None, \n", " style=\"r-\", \n", " data_style=\"b.\", \n", " data_label=None):\n", " \n", " x1 = np.linspace(axes[0], axes[1], 500)\n", " \n", " y_pred = sum(\n", " regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n", " \n", " ax.plot(X[:, 0], y, data_style, label=data_label)\n", " ax.plot(x1, y_pred, style, linewidth=2, label=label)\n", " if label or data_label:\n", " ax.legend(loc=\"upper center\", fontsize=16)\n", " ax.axis(axes)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2025-02-27T23:21:37.877341Z", "iopub.status.busy": "2025-02-27T23:21:37.877169Z", "iopub.status.idle": "2025-02-27T23:21:38.520264Z", "shell.execute_reply": "2025-02-27T23:21:38.519612Z" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, [[ax1, ax2], [ax3, ax4], [ax5, ax6]] = plt.subplots(3, 2, figsize=(11, 11))\n", "\n", "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], ax=ax1, label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n", "ax1.set_ylabel(\"$y$\", fontsize=16, rotation=0)\n", "ax1.set_title(\"Residuals and tree predictions\", fontsize=16)\n", "\n", "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], ax=ax2, label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n", "ax2.set_ylabel(\"$y$\", fontsize=16, rotation=0)\n", "ax2.set_title(\"Ensemble predictions\", fontsize=16)\n", "\n", "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], ax=ax3, label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\n", "ax3.set_ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n", "\n", "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], ax=ax4, label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n", "ax4.set_ylabel(\"$y$\", fontsize=16, rotation=0)\n", "\n", "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], ax=ax5, label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n", "ax5.set_ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n", "ax5.set_xlabel(\"$x_1$\", fontsize=16)\n", "\n", "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], ax=ax6, label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n", "ax6.set_xlabel(\"$x_1$\", fontsize=16)\n", "ax6.set_ylabel(\"$y$\", fontsize=16, rotation=0);" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "The ensemble’s predictions are equal to the sum of the predictions of the first two trees. Similarly, in the third row another tree is trained on the residual errors of the second tree. \n", "\n", "You can see that the ensemble’s predictions gradually get better as trees are added to the ensemble.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Now, let's use in-built functionality rather than implementing by hand." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2025-02-27T23:21:38.522279Z", "iopub.status.busy": "2025-02-27T23:21:38.522098Z", "iopub.status.idle": "2025-02-27T23:21:38.532656Z", "shell.execute_reply": "2025-02-27T23:21:38.532079Z" } }, "outputs": [ { "data": { "text/html": [ "
GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n",
       "                          random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n", " random_state=42)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import GradientBoostingRegressor\n", "\n", "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3,\n", " learning_rate=1.0, random_state=42)\n", "gbrt.fit(X, y)" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "For GBRT, the learning rate plays the role of scaling the contribution for each tree.\n", "\n", "Low values (e.g. 0.1) need more trees in ensemble to fit training set but predictions usually generalize better. This is called shrinkage.\n", "\n", "Can also consider early stopping to regularise." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:38.534498Z", "iopub.status.busy": "2025-02-27T23:21:38.534323Z", "iopub.status.idle": "2025-02-27T23:21:38.584825Z", "shell.execute_reply": "2025-02-27T23:21:38.584228Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,\n",
       "                          n_iter_no_change=10, random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "GradientBoostingRegressor(learning_rate=0.05, max_depth=2, n_estimators=500,\n", " n_iter_no_change=10, random_state=42)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gbrt_best = GradientBoostingRegressor(\n", " max_depth=2, learning_rate=0.05, n_estimators=500,\n", " n_iter_no_change=10, # Early stopping (set to None to turn off)\n", " random_state=42)\n", "gbrt_best.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:38.586570Z", "iopub.status.busy": "2025-02-27T23:21:38.586390Z", "iopub.status.idle": "2025-02-27T23:21:38.590237Z", "shell.execute_reply": "2025-02-27T23:21:38.589641Z" }, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "92" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gbrt_best.n_estimators_" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:38.591962Z", "iopub.status.busy": "2025-02-27T23:21:38.591770Z", "iopub.status.idle": "2025-02-27T23:21:38.595850Z", "shell.execute_reply": "2025-02-27T23:21:38.595265Z" }, "slideshow": { "slide_type": "skip" }, "tags": [] }, "outputs": [], "source": [ "def plot_predictions(regressors, X, y, axes, style,\n", " label=None, data_style=\"b.\", data_label=None):\n", " x1 = np.linspace(axes[0], axes[1], 500)\n", " y_pred = sum(regressor.predict(x1.reshape(-1, 1))\n", " for regressor in regressors)\n", " plt.plot(X[:, 0], y, data_style, label=data_label)\n", " plt.plot(x1, y_pred, style, linewidth=2, label=label)\n", " if label or data_label:\n", " plt.legend(loc=\"upper center\")\n", " plt.axis(axes)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "editable": true, "execution": { "iopub.execute_input": "2025-02-27T23:21:38.597668Z", "iopub.status.busy": "2025-02-27T23:21:38.597495Z", "iopub.status.idle": "2025-02-27T23:21:38.801956Z", "shell.execute_reply": "2025-02-27T23:21:38.801261Z" }, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n", "\n", "plt.sca(axes[0])\n", "plot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], style=\"r-\",\n", " label=\"Ensemble predictions\")\n", "plt.title(f\"learning_rate={gbrt.learning_rate}, \"\n", " f\"n_estimators={gbrt.n_estimators_}\")\n", "plt.xlabel(\"$x_1$\");\n", "plt.ylabel(\"$y$\", rotation=0);\n", "\n", "plt.sca(axes[1])\n", "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8], style=\"r-\")\n", "plt.title(f\"learning_rate={gbrt_best.learning_rate}, \"\n", " f\"n_estimators={gbrt_best.n_estimators_}\")\n", "plt.xlabel(\"$x_1$\");" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [ "exercise_pointer" ] }, "source": [ "**Exercises:** *You can now complete Exercise 1 in the exercises associated with this lecture.*" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Stacking (or meta learning)\n", "\n", "Stacking (stacked generalisation) introduces a learner to find the optimal combination, rather than a simple aggregation.\n", "\n", "Done by a _blender_ or _meta learner_.\n" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "\"data-layout\"\n", "\n", "[Source: Geron]" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Can extend to multiple blenders.\n", "\n", "\"data-layout\"\n", "\n", "[Source: Geron]" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 4 }