{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**7장 – 앙상블 학습과 랜덤 포레스트**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_이 노트북은 7장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", "
\n", " 구글 코랩에서 실행하기\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 설정" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "먼저 몇 개의 모듈을 임포트합니다. 맷플롯립 그래프를 인라인으로 출력하도록 만들고 그림을 저장하는 함수를 준비합니다. 또한 파이썬 버전이 3.5 이상인지 확인합니다(파이썬 2.x에서도 동작하지만 곧 지원이 중단되므로 파이썬 3을 사용하는 것이 좋습니다). 사이킷런 버전이 0.20 이상인지도 확인합니다." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# 파이썬 ≥3.5 필수\n", "import sys\n", "assert sys.version_info >= (3, 5)\n", "\n", "# 사이킷런 ≥0.20 필수\n", "import sklearn\n", "assert sklearn.__version__ >= \"0.20\"\n", "\n", "# 공통 모듈 임포트\n", "import numpy as np\n", "import os\n", "\n", "# 노트북 실행 결과를 동일하게 유지하기 위해\n", "np.random.seed(42)\n", "\n", "# 깔끔한 그래프 출력을 위해\n", "%matplotlib inline\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "mpl.rc('axes', labelsize=14)\n", "mpl.rc('xtick', labelsize=12)\n", "mpl.rc('ytick', labelsize=12)\n", "\n", "# 그림을 저장할 위치\n", "PROJECT_ROOT_DIR = \".\"\n", "CHAPTER_ID = \"ensembles\"\n", "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n", "os.makedirs(IMAGES_PATH, exist_ok=True)\n", "\n", "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n", " print(\"그림 저장:\", fig_id)\n", " if tight_layout:\n", " plt.tight_layout()\n", " plt.savefig(path, format=fig_extension, dpi=resolution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 투표기반 분류기" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "heads_proba = 0.51\n", "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n", "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<그림 7-3. 큰 수의 법칙> 생성 코드**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "그림 저장: law_of_large_numbers_plot\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,3.5))\n", "plt.plot(cumulative_heads_ratio)\n", "plt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\n", "plt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\n", "plt.xlabel(\"Number of coin tosses\")\n", "plt.ylabel(\"Heads ratio\")\n", "plt.legend(loc=\"lower right\")\n", "plt.axis([0, 10000, 0.42, 0.58])\n", "save_fig(\"law_of_large_numbers_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "moons 데이터셋을 사용해 보죠:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import make_moons\n", "\n", "X, y = make_moons(n_samples=500, noise=0.30, random_state=42)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**노트**: 향후 버전을 위해 사이킷런에서 기본 값이 될 `solver=\"lbfgs\"`, `n_estimators=100`, `gamma=\"scale\"`로 지정합니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**코드 예제:**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.ensemble import VotingClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "\n", "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n", "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", "svm_clf = SVC(gamma=\"scale\", random_state=42)\n", "\n", "voting_clf = VotingClassifier(\n", " estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n", " voting='hard')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n", " ('rf', RandomForestClassifier(random_state=42)),\n", " ('svc', SVC(random_state=42))])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voting_clf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LogisticRegression 0.864\n", "RandomForestClassifier 0.896\n", "SVC 0.896\n", "VotingClassifier 0.912\n" ] } ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n", " clf.fit(X_train, y_train)\n", " y_pred = clf.predict(X_test)\n", " print(clf.__class__.__name__, accuracy_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**노트**: 사이킷런 알고리즘이 이따금 업데이트되기 때문에 이 노트북의 결과가 책과 조금 다를 수 있습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "간접 투표:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n", " ('rf', RandomForestClassifier(random_state=42)),\n", " ('svc', SVC(probability=True, random_state=42))],\n", " voting='soft')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log_clf = LogisticRegression(solver=\"lbfgs\", random_state=42)\n", "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", "svm_clf = SVC(gamma=\"scale\", probability=True, random_state=42)\n", "\n", "voting_clf = VotingClassifier(\n", " estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n", " voting='soft')\n", "voting_clf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LogisticRegression 0.864\n", "RandomForestClassifier 0.896\n", "SVC 0.896\n", "VotingClassifier 0.92\n" ] } ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", "for clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n", " clf.fit(X_train, y_train)\n", " y_pred = clf.predict(X_test)\n", " print(clf.__class__.__name__, accuracy_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 배깅과 페이스팅" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 사이킷런의 배깅과 페이스팅" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "bag_clf = BaggingClassifier(\n", " DecisionTreeClassifier(), n_estimators=500,\n", " max_samples=100, bootstrap=True, random_state=42)\n", "bag_clf.fit(X_train, y_train)\n", "y_pred = bag_clf.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.904\n" ] } ], "source": [ "from sklearn.metrics import accuracy_score\n", "print(accuracy_score(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.856\n" ] } ], "source": [ "tree_clf = DecisionTreeClassifier(random_state=42)\n", "tree_clf.fit(X_train, y_train)\n", "y_pred_tree = tree_clf.predict(X_test)\n", "print(accuracy_score(y_test, y_pred_tree))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<그림 7-5. 단일 경정 트리(왼쪽)와 500개 트리로 만든 배깅 앙상블(오른쪽) 비교> 생성 코드**" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from matplotlib.colors import ListedColormap\n", "\n", "def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.5, contour=True):\n", " x1s = np.linspace(axes[0], axes[1], 100)\n", " x2s = np.linspace(axes[2], axes[3], 100)\n", " x1, x2 = np.meshgrid(x1s, x2s)\n", " X_new = np.c_[x1.ravel(), x2.ravel()]\n", " y_pred = clf.predict(X_new).reshape(x1.shape)\n", " custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n", " plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n", " if contour:\n", " custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n", " plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n", " plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n", " plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n", " plt.axis(axes)\n", " plt.xlabel(r\"$x_1$\", fontsize=18)\n", " plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "그림 저장: decision_tree_without_and_with_bagging_plot\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n", "plt.sca(axes[0])\n", "plot_decision_boundary(tree_clf, X, y)\n", "plt.title(\"Decision Tree\", fontsize=14)\n", "plt.sca(axes[1])\n", "plot_decision_boundary(bag_clf, X, y)\n", "plt.title(\"Decision Trees with Bagging\", fontsize=14)\n", "plt.ylabel(\"\")\n", "save_fig(\"decision_tree_without_and_with_bagging_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OOB 평가" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8986666666666666" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bag_clf = BaggingClassifier(\n", " DecisionTreeClassifier(), n_estimators=500,\n", " bootstrap=True, oob_score=True, random_state=40)\n", "bag_clf.fit(X_train, y_train)\n", "bag_clf.oob_score_" ] }, { 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],\n", " [0.00546448, 0.99453552],\n", " [0. , 1. ],\n", " [0.41836735, 0.58163265],\n", " [0.13095238, 0.86904762],\n", " [0.22110553, 0.77889447],\n", " [1. , 0. ],\n", " [0.97647059, 0.02352941],\n", " [0.21195652, 0.78804348],\n", " [0.98882682, 0.01117318],\n", " [0. , 1. ],\n", " [0. , 1. ],\n", " [1. , 0. ],\n", " [0.96428571, 0.03571429],\n", " [0.34554974, 0.65445026],\n", " [0.98235294, 0.01764706],\n", " [1. , 0. ],\n", " [0. , 1. ],\n", " [0.99465241, 0.00534759],\n", " [0. , 1. ],\n", " [0.06043956, 0.93956044],\n", " [0.98214286, 0.01785714],\n", " [1. , 0. ],\n", " [0.03108808, 0.96891192],\n", " [0.58854167, 0.41145833]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bag_clf.oob_decision_function_" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.912" ] }, "execution_count": 17, "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": {}, "source": [ "# 랜덤 포레스트" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, random_state=42)\n", "rnd_clf.fit(X_train, y_train)\n", "\n", "y_pred_rf = rnd_clf.predict(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "랜덤 포레스트는 결정 트리의 배깅과 비슷합니다:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "bag_clf = BaggingClassifier(\n", " DecisionTreeClassifier(max_features=\"sqrt\", max_leaf_nodes=16),\n", " n_estimators=500, random_state=42)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "bag_clf.fit(X_train, y_train)\n", "y_pred = bag_clf.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(y_pred == y_pred_rf) / len(y_pred) # 거의 에측이 동일합니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 특성 중요도" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "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": [ "from sklearn.datasets import load_iris\n", "iris = load_iris()\n", "rnd_clf = RandomForestClassifier(n_estimators=500, random_state=42)\n", "rnd_clf.fit(iris[\"data\"], iris[\"target\"])\n", "for name, score in zip(iris[\"feature_names\"], rnd_clf.feature_importances_):\n", " print(name, score)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.11249225, 0.02311929, 0.44103046, 0.423358 ])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd_clf.feature_importances_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "다음 그림은 15개 결정 트리의 결정 경계를 중첩한 것입니다. 여기서 볼 수 있듯이 개별 결정 트리는 불완전하지만 앙상블되면 매우 좋은 결정 경계를 만듭니다:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6, 4))\n", "\n", "for i in range(15):\n", " tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n", " indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n", " tree_clf.fit(X_train[indices_with_replacement], y_train[indices_with_replacement])\n", " plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.02, contour=False)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<그림 7-6. (랜덤 포레스트 분류기에서 얻은) MNIST 픽셀 중요도> 생성 코드**" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import fetch_openml\n", "\n", "mnist = fetch_openml('mnist_784', version=1)\n", "mnist.target = mnist.target.astype(np.uint8)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(random_state=42)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", "rnd_clf.fit(mnist[\"data\"], mnist[\"target\"])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "def plot_digit(data):\n", " image = data.reshape(28, 28)\n", " plt.imshow(image, cmap = mpl.cm.hot,\n", " interpolation=\"nearest\")\n", " plt.axis(\"off\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "그림 저장: mnist_feature_importance_plot\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_digit(rnd_clf.feature_importances_)\n", "\n", "cbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(), rnd_clf.feature_importances_.max()])\n", "cbar.ax.set_yticklabels(['Not important', 'Very important'])\n", "\n", "save_fig(\"mnist_feature_importance_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 부스팅\n", "\n", "## 에이다부스트" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n", " learning_rate=0.5, n_estimators=200, random_state=42)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import AdaBoostClassifier\n", "\n", "ada_clf = AdaBoostClassifier(\n", " DecisionTreeClassifier(max_depth=1), n_estimators=200,\n", " algorithm=\"SAMME.R\", learning_rate=0.5, random_state=42)\n", "ada_clf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_decision_boundary(ada_clf, X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<그림 7-8. 연속된 예측기의 결정 경계> 생성 코드**" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "그림 저장: boosting_plot\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "m = len(X_train)\n", "\n", "fig, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n", "for subplot, learning_rate in ((0, 1), (1, 0.5)):\n", " sample_weights = np.ones(m) / m\n", " plt.sca(axes[subplot])\n", " for i in range(5):\n", " svm_clf = SVC(kernel=\"rbf\", C=0.2, gamma=0.6, random_state=42)\n", " svm_clf.fit(X_train, y_train, sample_weight=sample_weights * m)\n", " y_pred = svm_clf.predict(X_train)\n", " r = sample_weights[y_pred != y_train].sum() / sample_weights.sum() # equation 7-1\n", " alpha = learning_rate * np.log((1 - r) / r) # equation 7-2\n", " sample_weights[y_pred != y_train] *= np.exp(alpha) # equation 7-3\n", " sample_weights /= sample_weights.sum() # normalization step\n", " plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n", " plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n", " if subplot == 0:\n", " plt.text(-0.7, -0.65, \"1\", fontsize=14)\n", " plt.text(-0.6, -0.10, \"2\", fontsize=14)\n", " plt.text(-0.5, 0.10, \"3\", fontsize=14)\n", " plt.text(-0.4, 0.55, \"4\", fontsize=14)\n", " plt.text(-0.3, 0.90, \"5\", fontsize=14)\n", " else:\n", " plt.ylabel(\"\")\n", "\n", "save_fig(\"boosting_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 그레이디언트 부스팅" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "간단한 이차식 형태의 데이터셋을 만들어 보죠:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "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": "markdown", "metadata": {}, "source": [ "이제 이 데이터셋에 결정 트리 회귀 모델을 훈련시킵니다:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeRegressor(max_depth=2, random_state=42)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", "tree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", "tree_reg1.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeRegressor(max_depth=2, random_state=42)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y2 = y - tree_reg1.predict(X)\n", "tree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", "tree_reg2.fit(X, y2)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeRegressor(max_depth=2, random_state=42)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y3 = y2 - tree_reg2.predict(X)\n", "tree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\n", "tree_reg3.fit(X, y3)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "X_new = np.array([[0.8]])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.75026781])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<그림 7-9> 생성 코드**" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "def plot_predictions(regressors, X, y, axes, label=None, style=\"r-\", 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)) 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\", fontsize=16)\n", " plt.axis(axes)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "그림 저장: gradient_boosting_plot\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(11,11))\n", "\n", "plt.subplot(321)\n", "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "plt.title(\"Residuals and tree predictions\", fontsize=16)\n", "\n", "plt.subplot(322)\n", "plot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "plt.title(\"Ensemble predictions\", fontsize=16)\n", "\n", "plt.subplot(323)\n", "plot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\n", "plt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n", "\n", "plt.subplot(324)\n", "plot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "\n", "plt.subplot(325)\n", "plot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\n", "plt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\n", "plt.xlabel(\"$x_1$\", fontsize=16)\n", "\n", "plt.subplot(326)\n", "plot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\n", "plt.xlabel(\"$x_1$\", fontsize=16)\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "\n", "save_fig(\"gradient_boosting_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 그레이디언트 부스팅 회귀 모델을 사용해 보죠:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n", " random_state=42)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import GradientBoostingRegressor\n", "\n", "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3, learning_rate=1.0, random_state=42)\n", "gbrt.fit(X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<그림 7-10. 예측기가 부족한 경우(왼쪽)과 너무 많은 경우(오른쪽)의 GBRT 앙상블> 생성 코드**" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GradientBoostingRegressor(max_depth=2, n_estimators=200, random_state=42)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gbrt_slow = GradientBoostingRegressor(max_depth=2, n_estimators=200, learning_rate=0.1, random_state=42)\n", "gbrt_slow.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "그림 저장: gbrt_learning_rate_plot\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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], label=\"Ensemble predictions\")\n", "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\n", "plt.xlabel(\"$x_1$\", fontsize=16)\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "\n", "plt.sca(axes[1])\n", "plot_predictions([gbrt_slow], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n", "plt.title(\"learning_rate={}, n_estimators={}\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\n", "plt.xlabel(\"$x_1$\", fontsize=16)\n", "\n", "save_fig(\"gbrt_learning_rate_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**조기 종료를 사용한 그래디언트 부스팅**" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GradientBoostingRegressor(max_depth=2, n_estimators=56, random_state=42)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error\n", "\n", "X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=49)\n", "\n", "gbrt = GradientBoostingRegressor(max_depth=2, n_estimators=120, random_state=42)\n", "gbrt.fit(X_train, y_train)\n", "\n", "errors = [mean_squared_error(y_val, y_pred)\n", " for y_pred in gbrt.staged_predict(X_val)]\n", "bst_n_estimators = np.argmin(errors) + 1\n", "\n", "gbrt_best = GradientBoostingRegressor(max_depth=2, n_estimators=bst_n_estimators, random_state=42)\n", "gbrt_best.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<그림 7-11. 조기 종료를 사용하여 트리 수 튜닝> 생성 코드**" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "min_error = np.min(errors)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "그림 저장: early_stopping_gbrt_plot\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 4))\n", "\n", "plt.subplot(121)\n", "plt.plot(np.arange(1, len(errors) + 1), errors, \"b.-\")\n", "plt.plot([bst_n_estimators, bst_n_estimators], [0, min_error], \"k--\")\n", "plt.plot([0, 120], [min_error, min_error], \"k--\")\n", "plt.plot(bst_n_estimators, min_error, \"ko\")\n", "plt.text(bst_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\n", "plt.axis([0, 120, 0, 0.01])\n", "plt.xlabel(\"Number of trees\")\n", "plt.ylabel(\"Error\", fontsize=16)\n", "plt.title(\"Validation error\", fontsize=14)\n", "\n", "plt.subplot(122)\n", "plot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\n", "plt.title(\"Best model (%d trees)\" % bst_n_estimators, fontsize=14)\n", "plt.ylabel(\"$y$\", fontsize=16, rotation=0)\n", "plt.xlabel(\"$x_1$\", fontsize=16)\n", "\n", "save_fig(\"early_stopping_gbrt_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "어느 정도 유예를 갖는 조기 종료(5 에포크 동안 향상되지 않을 때만 훈련 중지):" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "gbrt = GradientBoostingRegressor(max_depth=2, warm_start=True, random_state=42)\n", "\n", "min_val_error = float(\"inf\")\n", "error_going_up = 0\n", "for n_estimators in range(1, 120):\n", " gbrt.n_estimators = n_estimators\n", " gbrt.fit(X_train, y_train)\n", " y_pred = gbrt.predict(X_val)\n", " val_error = mean_squared_error(y_val, y_pred)\n", " if val_error < min_val_error:\n", " min_val_error = val_error\n", " error_going_up = 0\n", " else:\n", " error_going_up += 1\n", " if error_going_up == 5:\n", " break # early stopping" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "61\n" ] } ], "source": [ "print(gbrt.n_estimators)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minimum validation MSE: 0.002712853325235463\n" ] } ], "source": [ "print(\"Minimum validation MSE:\", min_val_error)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**XGBoost 사용하기**" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "try:\n", " import xgboost\n", "except ImportError as ex:\n", " print(\"에러: xgboost 라이브러리 설치되지 않았습니다.\")\n", " xgboost = None" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Validation MSE: 0.004000408205406276\n" ] } ], "source": [ "if xgboost is not None: # 책에 없음\n", " xgb_reg = xgboost.XGBRegressor(random_state=42)\n", " xgb_reg.fit(X_train, y_train)\n", " y_pred = xgb_reg.predict(X_val)\n", " val_error = mean_squared_error(y_val, y_pred) # 책에 없음\n", " print(\"Validation MSE:\", val_error) # 책에 없음" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\tvalidation_0-rmse:0.22834\n", "[1]\tvalidation_0-rmse:0.16224\n", "[2]\tvalidation_0-rmse:0.11843\n", "[3]\tvalidation_0-rmse:0.08760\n", "[4]\tvalidation_0-rmse:0.06848\n", "[5]\tvalidation_0-rmse:0.05709\n", "[6]\tvalidation_0-rmse:0.05297\n", "[7]\tvalidation_0-rmse:0.05129\n", "[8]\tvalidation_0-rmse:0.05155\n", "[9]\tvalidation_0-rmse:0.05211\n", "Validation MSE: 0.002630868681577655\n" ] } ], "source": [ "if xgboost is not None: # 책에 없음\n", " xgb_reg.fit(X_train, y_train,\n", " eval_set=[(X_val, y_val)], early_stopping_rounds=2)\n", " y_pred = xgb_reg.predict(X_val)\n", " val_error = mean_squared_error(y_val, y_pred) # 책에 없음\n", " print(\"Validation MSE:\", val_error) # 책에 없음" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1min 12s ± 2.2 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "44.4 ms ± 8.73 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "%timeit GradientBoostingRegressor().fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 연습문제 해답" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. to 7." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "부록 A 참조." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. 투표 기반 분류기" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "문제: _MNIST 데이터를 불러들여 훈련 세트, 검증 세트, 테스트 세트로 나눕니다(예를 들면 훈련에 40,000개 샘플, 검증에 10,000개 샘플, 테스트에 10,000개 샘플)._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MNIST 데이터셋은 앞에서 로드했습니다." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "X_train_val, X_test, y_train_val, y_test = train_test_split(\n", " mnist.data, mnist.target, test_size=10000, random_state=42)\n", "X_train, X_val, y_train, y_val = train_test_split(\n", " X_train_val, y_train_val, test_size=10000, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "문제: _그런 다음 랜덤 포레스트 분류기, 엑스트라 트리 분류기, SVM 같은 여러 종류의 분류기를 훈련시킵니다._" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n", "from sklearn.svm import LinearSVC\n", "from sklearn.neural_network import MLPClassifier" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "random_forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n", "extra_trees_clf = ExtraTreesClassifier(n_estimators=100, random_state=42)\n", "svm_clf = LinearSVC(max_iter=100, tol=20, random_state=42)\n", "mlp_clf = MLPClassifier(random_state=42)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training the RandomForestClassifier(random_state=42)\n", "Training the ExtraTreesClassifier(random_state=42)\n", "Training the LinearSVC(max_iter=100, random_state=42, tol=20)\n", "Training the MLPClassifier(random_state=42)\n" ] } ], "source": [ "estimators = [random_forest_clf, extra_trees_clf, svm_clf, mlp_clf]\n", "for estimator in estimators:\n", " print(\"Training the\", estimator)\n", " estimator.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.9692, 0.9715, 0.859, 0.9666]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[estimator.score(X_val, y_val) for estimator in estimators]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "선형 SVM이 다른 분류기보다 성능이 많이 떨어집니다. 그러나 투표 기반 분류기의 성능을 향상시킬 수 있으므로 그대로 두겠습니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "문제: _그리고 검증 세트에서 개개의 분류기보다 더 높은 성능을 내도록 이들을 간접 또는 직접 투표 분류기를 사용하는 앙상블로 연결해보세요._" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import VotingClassifier" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "named_estimators = [\n", " (\"random_forest_clf\", random_forest_clf),\n", " (\"extra_trees_clf\", extra_trees_clf),\n", " (\"svm_clf\", svm_clf),\n", " (\"mlp_clf\", mlp_clf),\n", "]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "voting_clf = VotingClassifier(named_estimators)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "VotingClassifier(estimators=[('random_forest_clf',\n", " RandomForestClassifier(random_state=42)),\n", " ('extra_trees_clf',\n", " ExtraTreesClassifier(random_state=42)),\n", " ('svm_clf',\n", " LinearSVC(max_iter=100, random_state=42, tol=20)),\n", " ('mlp_clf', MLPClassifier(random_state=42))])" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voting_clf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9708" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voting_clf.score(X_val, y_val)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.9692, 0.9715, 0.859, 0.9666]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[estimator.score(X_val, y_val) for estimator in voting_clf.estimators_]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SVM 모델을 제거해서 성능이 향상되는지 확인해 보죠. 다음과 같이 `set_params()`를 사용하여 `None`으로 지정하면 특정 예측기를 제외시킬 수 있습니다:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "VotingClassifier(estimators=[('random_forest_clf',\n", " RandomForestClassifier(random_state=42)),\n", " ('extra_trees_clf',\n", " ExtraTreesClassifier(random_state=42)),\n", " ('svm_clf', None),\n", " ('mlp_clf', MLPClassifier(random_state=42))])" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voting_clf.set_params(svm_clf=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "예측기 목록이 업데이트되었습니다:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('random_forest_clf', RandomForestClassifier(random_state=42)),\n", " ('extra_trees_clf', ExtraTreesClassifier(random_state=42)),\n", " ('svm_clf', None),\n", " ('mlp_clf', MLPClassifier(random_state=42))]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voting_clf.estimators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "하지만 훈련된 예측기 목록은 업데이트되지 않습니다:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[RandomForestClassifier(random_state=42),\n", " ExtraTreesClassifier(random_state=42),\n", " LinearSVC(max_iter=100, random_state=42, tol=20),\n", " MLPClassifier(random_state=42)]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voting_clf.estimators_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`VotingClassifier`를 다시 훈련시키거나 그냥 훈련된 예측기 목록에서 SVM 모델을 제거할 수 있습니다:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "del voting_clf.estimators_[2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`VotingClassifier`를 다시 평가해 보죠:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9741" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voting_clf.score(X_val, y_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "훨씬 나아졌네요! SVM 모델이 성능을 저하시켰습니다. 이제 간접 투표 분류기를 사용해 보죠. 분류기를 다시 훈련시킬 필요는 없고 `voting`을 `\"soft\"`로 지정하면 됩니다:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "voting_clf.voting = \"soft\"" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.972" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voting_clf.score(X_val, y_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 경우는 직접 투표 방식이 낫네요." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_앙상블을 얻고 나면 테스트 세트로 확인해보세요. 개개의 분류기와 비교해서 성능이 얼마나 향상되나요?_" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.971" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "voting_clf.voting = \"hard\"\n", "voting_clf.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.9645, 0.9691, 0.9643]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[estimator.score(X_test, y_test) for estimator in voting_clf.estimators_]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "여기서는 투표 기반 분류기가 최선의 모델의 오차율을 아주 조금만 감소시킵니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 9. 스태킹 앙상블" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "문제: _이전 연습문제의 각 분류기를 실행해서 검증 세트에서 예측을 만들고 그 결과로 새로운 훈련 세트를 만들어보세요. 각 훈련 샘플은 하나의 이미지에 대한 전체 분류기의 예측을 담은 벡터고 타깃은 이미지의 클래스입니다. 새로운 훈련 세트에 분류기 하나를 훈련시켜 보세요._" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "X_val_predictions = np.empty((len(X_val), len(estimators)), dtype=np.float32)\n", "\n", "for index, estimator in enumerate(estimators):\n", " X_val_predictions[:, index] = estimator.predict(X_val)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5., 5., 5., 5.],\n", " [8., 8., 8., 8.],\n", " [2., 2., 3., 2.],\n", " ...,\n", " [7., 7., 7., 7.],\n", " [6., 6., 6., 6.],\n", " [7., 7., 7., 7.]], dtype=float32)" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_val_predictions" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd_forest_blender = RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)\n", "rnd_forest_blender.fit(X_val_predictions, y_val)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9707" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd_forest_blender.oob_score_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 블렌더를 세밀하게 튜닝하거나 다른 종류의 블렌더(예를 들어, `MLPClassifier`)를 시도해 볼 수 있습니다. 그런 늘 하던대로 다음 교차 검증을 사용해 가장 좋은 것을 선택합니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "문제: _축하합니다. 방금 블렌더를 훈련시켰습니다. 그리고 이 분류기를 모아서 스태킹 앙상블을 구성했습니다. 이제 테스트 세트에 앙상블을 평가해보세요. 테스트 세트의 각 이미지에 대해 모든 분류기로 예측을 만들고 앙상블의 예측 결과를 만들기 위해 블렌더에 그 예측을 주입합니다. 앞서 만든 투표 분류기와 비교하면 어떤가요?_" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "X_test_predictions = np.empty((len(X_test), len(estimators)), dtype=np.float32)\n", "\n", "for index, estimator in enumerate(estimators):\n", " X_test_predictions[:, index] = estimator.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "y_pred = rnd_forest_blender.predict(X_test_predictions)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9695" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy_score(y_test, y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 스태킹 앙상블은 앞서 만든 투표 기반 분류기만큼 성능을 내지는 못합니다. 최선의 개별 분류기만큼 뛰어나지는 않습니다." ] } ], "metadata": { "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": "3.7.3" }, "nav_menu": { "height": "252px", "width": "333px" }, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }