{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPython 3.7.3\n", "IPython 7.5.0\n", "\n", "numpy 1.16.3\n", "scipy 1.2.1\n", "sklearn 0.21.1\n", "pandas 0.24.2\n", "matplotlib 3.0.3\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -v -p numpy,scipy,sklearn,pandas,matplotlib" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**5 장 – 서포트 벡터 머신**\n", "\n", "_이 노트북은 2장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 설정" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# 파이썬 2와 파이썬 3 지원\n", "from __future__ import division, print_function, unicode_literals\n", "\n", "# 공통\n", "import numpy as np\n", "import os\n", "\n", "# 일관된 출력을 위해 유사난수 초기화\n", "np.random.seed(42)\n", "\n", "# 맷플롯립 설정\n", "%matplotlib inline\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['axes.labelsize'] = 14\n", "plt.rcParams['xtick.labelsize'] = 12\n", "plt.rcParams['ytick.labelsize'] = 12\n", "\n", "# 한글출력\n", "plt.rcParams['font.family'] = 'NanumBarunGothic'\n", "plt.rcParams['axes.unicode_minus'] = False\n", "\n", "# 그림을 저장할 폴드\n", "PROJECT_ROOT_DIR = \".\"\n", "CHAPTER_ID = \"svm\"\n", "\n", "def save_fig(fig_id, tight_layout=True):\n", " path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n", " if tight_layout:\n", " plt.tight_layout()\n", " plt.savefig(path, format='png', dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 라지 마진 분류" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "다음 몇 개의 코드 셀은 5장 앞부분의 그래프를 만듭니다. 실제 코드 예제는 그 이후에 나옵니다:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n", " kernel='linear', max_iter=-1, probability=False, random_state=None,\n", " shrinking=True, tol=0.001, verbose=False)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import SVC\n", "from sklearn import datasets\n", "\n", "iris = datasets.load_iris()\n", "X = iris[\"data\"][:, (2, 3)] # 꽃잎 길이, 꽃잎 너비\n", "y = iris[\"target\"]\n", "\n", "setosa_or_versicolor = (y == 0) | (y == 1)\n", "X = X[setosa_or_versicolor]\n", "y = y[setosa_or_versicolor]\n", "\n", "# SVM 분류 모델\n", "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n", "svm_clf.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 나쁜 모델\n", "x0 = np.linspace(0, 5.5, 200)\n", "pred_1 = 5*x0 - 20\n", "pred_2 = x0 - 1.8\n", "pred_3 = 0.1 * x0 + 0.5\n", "\n", "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n", " w = svm_clf.coef_[0]\n", " b = svm_clf.intercept_[0]\n", "\n", " # 결정 경계에서 w0*x0 + w1*x1 + b = 0 이므로\n", " # => x1 = -w0/w1 * x0 - b/w1\n", " x0 = np.linspace(xmin, xmax, 200)\n", " decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n", "\n", " margin = 1/w[1]\n", " gutter_up = decision_boundary + margin\n", " gutter_down = decision_boundary - margin\n", "\n", " svs = svm_clf.support_vectors_\n", " plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n", " plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n", " plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n", " plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n", "\n", "plt.figure(figsize=(12,2.7))\n", "\n", "plt.subplot(121)\n", "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n", "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n", "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n", "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n", "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.ylabel(\"꽃잎 너비\", fontsize=14)\n", "plt.legend(loc=\"upper left\", fontsize=14)\n", "plt.axis([0, 5.5, 0, 2])\n", "\n", "plt.subplot(122)\n", "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n", "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n", "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.axis([0, 5.5, 0, 2])\n", "\n", "save_fig(\"large_margin_classification_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 특성의 스케일에 민감함" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n", "ys = np.array([0, 0, 1, 1])\n", "svm_clf = SVC(kernel=\"linear\", C=100)\n", "svm_clf.fit(Xs, ys)\n", "\n", "plt.figure(figsize=(12,3.2))\n", "plt.subplot(121)\n", "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n", "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n", "plot_svc_decision_boundary(svm_clf, 0, 6)\n", "plt.xlabel(\"$x_0$\", fontsize=20)\n", "plt.ylabel(\"$x_1$ \", fontsize=20, rotation=0)\n", "plt.title(\"스케일 조정 전\", fontsize=16)\n", "plt.axis([0, 6, 0, 90])\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "X_scaled = scaler.fit_transform(Xs)\n", "svm_clf.fit(X_scaled, ys)\n", "\n", "plt.subplot(122)\n", "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n", "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n", "plot_svc_decision_boundary(svm_clf, -2, 2)\n", "plt.xlabel(\"$x_0$\", fontsize=20)\n", "plt.title(\"스케일 조정 후\", fontsize=16)\n", "plt.axis([-2, 2, -2, 2])\n", "\n", "save_fig(\"sensitivity_to_feature_scales_plot\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 이상치에 민감함" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n", "y_outliers = np.array([0, 0])\n", "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n", "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n", "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n", "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n", "\n", "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n", "svm_clf2.fit(Xo2, yo2)\n", "\n", "plt.figure(figsize=(12,2.7))\n", "\n", "plt.subplot(121)\n", "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n", "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n", "plt.text(0.3, 1.0, \"불가능!\", fontsize=24, color=\"red\")\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.ylabel(\"꽃잎 너비\", fontsize=14)\n", "plt.annotate(\"이상치\",\n", " xy=(X_outliers[0][0], X_outliers[0][1]),\n", " xytext=(2.5, 1.7),\n", " ha=\"center\",\n", " arrowprops=dict(facecolor='black', shrink=0.1),\n", " fontsize=16,\n", " )\n", "plt.axis([0, 5.5, 0, 2])\n", "\n", "plt.subplot(122)\n", "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n", "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n", "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.annotate(\"이상치\",\n", " xy=(X_outliers[1][0], X_outliers[1][1]),\n", " xytext=(3.2, 0.08),\n", " ha=\"center\",\n", " arrowprops=dict(facecolor='black', shrink=0.1),\n", " fontsize=16,\n", " )\n", "plt.axis([0, 5.5, 0, 2])\n", "\n", "save_fig(\"sensitivity_to_outliers_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 라지 마진 *vs* 마진 오류" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "다음이 5장의 첫 번째 코드 예제입니다:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(memory=None,\n", " steps=[('scaler',\n", " StandardScaler(copy=True, with_mean=True, with_std=True)),\n", " ('linear_svc',\n", " LinearSVC(C=1, class_weight=None, dual=True,\n", " fit_intercept=True, intercept_scaling=1,\n", " loss='hinge', max_iter=1000, multi_class='ovr',\n", " penalty='l2', random_state=42, tol=0.0001,\n", " verbose=0))],\n", " verbose=False)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from sklearn import datasets\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.svm import LinearSVC\n", "\n", "iris = datasets.load_iris()\n", "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n", "y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica\n", "\n", "svm_clf = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\", random_state=42)),\n", " ])\n", "\n", "svm_clf.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svm_clf.predict([[5.5, 1.7]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "여러가지 규제 설정을 비교하는 그래프를 만들겠습니다:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(memory=None,\n", " steps=[('scaler',\n", " StandardScaler(copy=True, with_mean=True, with_std=True)),\n", " ('linear_svc',\n", " LinearSVC(C=100, class_weight=None, dual=True,\n", " fit_intercept=True, intercept_scaling=1,\n", " loss='hinge', max_iter=1000, multi_class='ovr',\n", " penalty='l2', random_state=42, tol=0.0001,\n", " verbose=0))],\n", " verbose=False)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaler = StandardScaler()\n", "svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n", "svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n", "\n", "scaled_svm_clf1 = Pipeline([\n", " (\"scaler\", scaler),\n", " (\"linear_svc\", svm_clf1),\n", " ])\n", "scaled_svm_clf2 = Pipeline([\n", " (\"scaler\", scaler),\n", " (\"linear_svc\", svm_clf2),\n", " ])\n", "\n", "scaled_svm_clf1.fit(X, y)\n", "scaled_svm_clf2.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# 스케일되지 않은 파라미터로 변경\n", "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n", "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n", "w1 = svm_clf1.coef_[0] / scaler.scale_\n", "w2 = svm_clf2.coef_[0] / scaler.scale_\n", "svm_clf1.intercept_ = np.array([b1])\n", "svm_clf2.intercept_ = np.array([b2])\n", "svm_clf1.coef_ = np.array([w1])\n", "svm_clf2.coef_ = np.array([w2])\n", "\n", "# 서포트 벡터 찾기 (libsvm과 달리 liblinear 라이브러리에서 제공하지 않기 때문에 \n", "# LinearSVC에는 서포트 벡터가 저장되어 있지 않습니다.)\n", "t = y * 2 - 1\n", "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n", "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n", "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n", "svm_clf2.support_vectors_ = X[support_vectors_idx2]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,3.2))\n", "plt.subplot(121)\n", "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris-Virginica\")\n", "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolor\")\n", "plot_svc_decision_boundary(svm_clf1, 4, 6)\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.ylabel(\"꽃잎 너비\", fontsize=14)\n", "plt.legend(loc=\"upper left\", fontsize=14)\n", "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n", "plt.axis([4, 6, 0.8, 2.8])\n", "\n", "plt.subplot(122)\n", "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n", "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n", "plot_svc_decision_boundary(svm_clf2, 4, 6)\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n", "plt.axis([4, 6, 0.8, 2.8])\n", "\n", "save_fig(\"regularization_plot\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 비선형 분류" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n", "X2D = np.c_[X1D, X1D**2]\n", "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n", "\n", "plt.figure(figsize=(11, 4))\n", "\n", "plt.subplot(121)\n", "plt.grid(True, which='both')\n", "plt.axhline(y=0, color='k')\n", "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n", "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n", "plt.gca().get_yaxis().set_ticks([])\n", "plt.xlabel(r\"$x_1$\", fontsize=20)\n", "plt.axis([-4.5, 4.5, -0.2, 0.2])\n", "\n", "plt.subplot(122)\n", "plt.grid(True, which='both')\n", "plt.axhline(y=0, color='k')\n", "plt.axvline(x=0, color='k')\n", "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n", "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n", "plt.xlabel(r\"$x_1$\", fontsize=20)\n", "plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n", "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n", "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n", "plt.axis([-4.5, 4.5, -1, 17])\n", "\n", "plt.subplots_adjust(right=1)\n", "\n", "save_fig(\"higher_dimensions_plot\", tight_layout=False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import make_moons\n", "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n", "\n", "def plot_dataset(X, y, axes):\n", " plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n", " plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n", " plt.axis(axes)\n", " plt.grid(True, which='both')\n", " plt.xlabel(r\"$x_1$\", fontsize=20)\n", " plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n", "\n", "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`LinearSVC`와 `LogisticRegression(solver='liblinear')`가 `max_iter` 반복 안에 수렴하지 않고 verbose 매개변수가 0이 아닐 때 반복 횟수를 증가하라는 경고 메세지가 나옵니다. 사이킷런 0.20 버전부터는 `verbose` 매개변수에 상관없이 `max_iter` 반복 안에 수렴하지 않을 경우 반복 횟수 증가 경고가 나옵니다. 경고 메세지를 피하기 위해 `max_iter` 매개변수의 기본값을 1,000에서 2,000으로 증가시킵니다." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(memory=None,\n", " steps=[('poly_features',\n", " PolynomialFeatures(degree=3, include_bias=True,\n", " interaction_only=False, order='C')),\n", " ('scaler',\n", " StandardScaler(copy=True, with_mean=True, with_std=True)),\n", " ('svm_clf',\n", " LinearSVC(C=10, class_weight=None, dual=True,\n", " fit_intercept=True, intercept_scaling=1,\n", " loss='hinge', max_iter=2000, multi_class='ovr',\n", " penalty='l2', random_state=42, tol=0.0001,\n", " verbose=0))],\n", " verbose=False)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.datasets import make_moons\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import PolynomialFeatures\n", "\n", "polynomial_svm_clf = Pipeline([\n", " (\"poly_features\", PolynomialFeatures(degree=3)),\n", " (\"scaler\", StandardScaler()),\n", " (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", max_iter=2000, random_state=42))\n", " ])\n", "\n", "polynomial_svm_clf.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_predictions(clf, axes):\n", " x0s = np.linspace(axes[0], axes[1], 100)\n", " x1s = np.linspace(axes[2], axes[3], 100)\n", " x0, x1 = np.meshgrid(x0s, x1s)\n", " X = np.c_[x0.ravel(), x1.ravel()]\n", " y_pred = clf.predict(X).reshape(x0.shape)\n", " y_decision = clf.decision_function(X).reshape(x0.shape)\n", " plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n", " plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n", "\n", "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n", "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n", "\n", "save_fig(\"moons_polynomial_svc_plot\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(memory=None,\n", " steps=[('scaler',\n", " StandardScaler(copy=True, with_mean=True, with_std=True)),\n", " ('svm_clf',\n", " SVC(C=5, cache_size=200, class_weight=None, coef0=1,\n", " decision_function_shape='ovr', degree=3,\n", " gamma='auto_deprecated', kernel='poly', max_iter=-1,\n", " probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False))],\n", " verbose=False)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import SVC\n", "\n", "poly_kernel_svm_clf = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n", " ])\n", "poly_kernel_svm_clf.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(memory=None,\n", " steps=[('scaler',\n", " StandardScaler(copy=True, with_mean=True, with_std=True)),\n", " ('svm_clf',\n", " SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n", " decision_function_shape='ovr', degree=10,\n", " gamma='auto_deprecated', kernel='poly', max_iter=-1,\n", " probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False))],\n", " verbose=False)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poly100_kernel_svm_clf = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n", " ])\n", "poly100_kernel_svm_clf.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(11, 4))\n", "\n", "plt.subplot(121)\n", "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n", "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n", "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n", "\n", "plt.subplot(122)\n", "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n", "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n", "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n", "\n", "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def gaussian_rbf(x, landmark, gamma):\n", " return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n", "\n", "gamma = 0.3\n", "\n", "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n", "x2s = gaussian_rbf(x1s, -2, gamma)\n", "x3s = gaussian_rbf(x1s, 1, gamma)\n", "\n", "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n", "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n", "\n", "plt.figure(figsize=(11, 4))\n", "\n", "plt.subplot(121)\n", "plt.grid(True, which='both')\n", "plt.axhline(y=0, color='k')\n", "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n", "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n", "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n", "plt.plot(x1s, x2s, \"g--\")\n", "plt.plot(x1s, x3s, \"b:\")\n", "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n", "plt.xlabel(r\"$x_1$\", fontsize=20)\n", "plt.ylabel(r\"유사도\", fontsize=14)\n", "plt.annotate(r'$\\mathbf{x}$',\n", " xy=(X1D[3, 0], 0),\n", " xytext=(-0.5, 0.20),\n", " ha=\"center\",\n", " arrowprops=dict(facecolor='black', shrink=0.1),\n", " fontsize=18,\n", " )\n", "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n", "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n", "plt.axis([-4.5, 4.5, -0.1, 1.1])\n", "\n", "plt.subplot(122)\n", "plt.grid(True, which='both')\n", "plt.axhline(y=0, color='k')\n", "plt.axvline(x=0, color='k')\n", "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n", "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n", "plt.xlabel(r\"$x_2$\", fontsize=20)\n", "plt.ylabel(r\"$x_3$ \", fontsize=20, rotation=0)\n", "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n", " xy=(XK[3, 0], XK[3, 1]),\n", " xytext=(0.65, 0.50),\n", " ha=\"center\",\n", " arrowprops=dict(facecolor='black', shrink=0.1),\n", " fontsize=18,\n", " )\n", "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n", "plt.axis([-0.1, 1.1, -0.1, 1.1])\n", " \n", "plt.subplots_adjust(right=1)\n", "\n", "save_fig(\"kernel_method_plot\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Phi(-1.0, -2) = [0.74081822]\n", "Phi(-1.0, 1) = [0.30119421]\n" ] } ], "source": [ "x1_example = X1D[3, 0]\n", "for landmark in (-2, 1):\n", " k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n", " print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(memory=None,\n", " steps=[('scaler',\n", " StandardScaler(copy=True, with_mean=True, with_std=True)),\n", " ('svm_clf',\n", " SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma=5,\n", " kernel='rbf', max_iter=-1, probability=False,\n", " random_state=None, shrinking=True, tol=0.001,\n", " verbose=False))],\n", " verbose=False)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rbf_kernel_svm_clf = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n", " ])\n", "rbf_kernel_svm_clf.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.svm import SVC\n", "\n", "gamma1, gamma2 = 0.1, 5\n", "C1, C2 = 0.001, 1000\n", "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n", "\n", "svm_clfs = []\n", "for gamma, C in hyperparams:\n", " rbf_kernel_svm_clf = Pipeline([\n", " (\"scaler\", StandardScaler()),\n", " (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n", " ])\n", " rbf_kernel_svm_clf.fit(X, y)\n", " svm_clfs.append(rbf_kernel_svm_clf)\n", "\n", "plt.figure(figsize=(11, 7))\n", "\n", "for i, svm_clf in enumerate(svm_clfs):\n", " plt.subplot(221 + i)\n", " plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n", " plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n", " gamma, C = hyperparams[i]\n", " plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n", "\n", "save_fig(\"moons_rbf_svc_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 회귀" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "np.random.seed(42)\n", "m = 50\n", "X = 2 * np.random.rand(m, 1)\n", "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\n", " intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n", " random_state=42, tol=0.0001, verbose=0)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import LinearSVR\n", "\n", "svm_reg = LinearSVR(epsilon=1.5, random_state=42)\n", "svm_reg.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n", "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n", "svm_reg1.fit(X, y)\n", "svm_reg2.fit(X, y)\n", "\n", "def find_support_vectors(svm_reg, X, y):\n", " y_pred = svm_reg.predict(X)\n", " off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n", " return np.argwhere(off_margin)\n", "\n", "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n", "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n", "\n", "eps_x1 = 1\n", "eps_y_pred = svm_reg1.predict([[eps_x1]])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_svm_regression(svm_reg, X, y, axes):\n", " x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n", " y_pred = svm_reg.predict(x1s)\n", " plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n", " plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n", " plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n", " plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n", " plt.plot(X, y, \"bo\")\n", " plt.xlabel(r\"$x_1$\", fontsize=18)\n", " plt.legend(loc=\"upper left\", fontsize=18)\n", " plt.axis(axes)\n", "\n", "plt.figure(figsize=(9, 4))\n", "plt.subplot(121)\n", "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n", "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n", "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n", "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n", "plt.annotate(\n", " '', xy=(eps_x1, eps_y_pred), xycoords='data',\n", " xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n", " textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n", " )\n", "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n", "plt.subplot(122)\n", "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n", "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n", "save_fig(\"svm_regression_plot\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "np.random.seed(42)\n", "m = 100\n", "X = 2 * np.random.rand(m, 1) - 1\n", "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "사이킷런 0.20 버전에서 `SVC`, `SVR` 클래스의 `gamma` 매개변수 옵션에 `auto`외에 `scale`이 추가되었습니다. `auto`는 `1/n_features`, 즉 특성 개수의 역수입니다. `scale`은 `1/(n_features * X.std())`로 스케일 조정이 되지 않은 특성에서 더 좋은 결과를 만듭니다. 사이킷런 0.22 버전부터는 `gamma` 매개변수의 기본값이 `auto`에서 `scale`로 변경됩니다. 서포트 벡터 머신을 사용하기 전에 특성을 표준화 전처리하면 `scale`과 `auto`는 차이가 없습니다. 경고를 피하기 위해 명시적으로 `auto` 옵션을 지정합니다." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n", " kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import SVR\n", "\n", "svm_poly_reg = SVR(kernel=\"poly\", gamma='auto', degree=2, C=100, epsilon=0.1)\n", "svm_poly_reg.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n", " kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import SVR\n", "\n", "svm_poly_reg1 = SVR(kernel=\"poly\", gamma='auto', degree=2, C=100, epsilon=0.1)\n", "svm_poly_reg2 = SVR(kernel=\"poly\", gamma='auto', degree=2, C=0.01, epsilon=0.1)\n", "svm_poly_reg1.fit(X, y)\n", "svm_poly_reg2.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(9, 4))\n", "plt.subplot(121)\n", "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n", "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n", "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n", "plt.subplot(122)\n", "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n", "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n", "save_fig(\"svm_with_polynomial_kernel_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SVM 이론" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "iris = datasets.load_iris()\n", "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n", "y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.mplot3d import Axes3D\n", "\n", "def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n", " x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n", " X_crop = X[x1_in_bounds]\n", " y_crop = y[x1_in_bounds]\n", " x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n", " x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n", " x1, x2 = np.meshgrid(x1s, x2s)\n", " xs = np.c_[x1.ravel(), x2.ravel()]\n", " df = (xs.dot(w) + b).reshape(x1.shape)\n", " m = 1 / np.linalg.norm(w)\n", " boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n", " margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n", " margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n", " ax.plot_surface(x1s, x2, np.zeros_like(x1),\n", " color=\"b\", alpha=0.2, cstride=100, rstride=100)\n", " ax.plot(x1s, boundary_x2s, 0, \"k-\", linewidth=2, label=r\"$h=0$\")\n", " ax.plot(x1s, margin_x2s_1, 0, \"k--\", linewidth=2, label=r\"$h=\\pm 1$\")\n", " ax.plot(x1s, margin_x2s_2, 0, \"k--\", linewidth=2)\n", " ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \"g^\")\n", " ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\"k\")\n", " ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \"bs\")\n", " ax.axis(x1_lim + x2_lim)\n", " ax.text(4.5, 2.5, 3.8, \"결정 함수 $h$\", fontsize=15)\n", " ax.set_xlabel(r\"꽃잎 길이\", fontsize=15, labelpad=15)\n", " ax.set_ylabel(r\"꽃잎 너비\", fontsize=15, rotation=25, labelpad=15)\n", " ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18, labelpad=10)\n", " ax.legend(loc=\"upper left\", fontsize=16)\n", "\n", "fig = plt.figure(figsize=(11, 6))\n", "ax1 = fig.add_subplot(111, projection='3d')\n", "plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n", "\n", "save_fig(\"iris_3D_plot\", tight_layout=False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 작은 가중치 벡터가 라지 마진을 만듭니다" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\n", " x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\n", " y = w * x1 + b\n", " m = 1 / w\n", "\n", " plt.plot(x1, y)\n", " plt.plot(x1_lim, [1, 1], \"k:\")\n", " plt.plot(x1_lim, [-1, -1], \"k:\")\n", " plt.axhline(y=0, color='k')\n", " plt.axvline(x=0, color='k')\n", " plt.plot([m, m], [0, 1], \"k--\")\n", " plt.plot([-m, -m], [0, -1], \"k--\")\n", " plt.plot([-m, m], [0, 0], \"k-o\", linewidth=3)\n", " plt.axis(x1_lim + [-2, 2])\n", " plt.xlabel(r\"$x_1$\", fontsize=16)\n", " if ylabel:\n", " plt.ylabel(r\"$w_1 x_1$ \", rotation=0, fontsize=16)\n", " plt.title(r\"$w_1 = {}$\".format(w), fontsize=16)\n", "\n", "plt.figure(figsize=(12, 3.2))\n", "plt.subplot(121)\n", "plot_2D_decision_function(1, 0)\n", "plt.subplot(122)\n", "plot_2D_decision_function(0.5, 0, ylabel=False)\n", "save_fig(\"small_w_large_margin_plot\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import SVC\n", "from sklearn import datasets\n", "\n", "iris = datasets.load_iris()\n", "X = iris[\"data\"][:, (2, 3)] # 꽃잎 길이, 꽃잎 너비\n", "y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica\n", "\n", "svm_clf = SVC(kernel=\"linear\", C=1)\n", "svm_clf.fit(X, y)\n", "svm_clf.predict([[5.3, 1.3]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 힌지 손실" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t = np.linspace(-2, 4, 200)\n", "h = np.where(1 - t < 0, 0, 1 - t) # max(0, 1-t)\n", "\n", "plt.figure(figsize=(5,2.8))\n", "plt.plot(t, h, \"b-\", linewidth=2, label=\"$max(0, 1 - t)$\")\n", "plt.grid(True, which='both')\n", "plt.axhline(y=0, color='k')\n", "plt.axvline(x=0, color='k')\n", "plt.yticks(np.arange(-1, 2.5, 1))\n", "plt.xlabel(\"$t$\", fontsize=16)\n", "plt.axis([-2, 4, -1, 2.5])\n", "plt.legend(loc=\"upper right\", fontsize=16)\n", "save_fig(\"hinge_plot\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 추가 내용" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 훈련 시간" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\n", "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n", "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[LibSVM]0 0.1 0.18552899360656738\n", "[LibSVM]1 0.01 0.18365263938903809\n", "[LibSVM]2 0.001 0.21489548683166504\n", "[LibSVM]3 0.0001 0.39548420906066895\n", "[LibSVM]4 1e-05 0.639998197555542\n", "[LibSVM]5 1.0000000000000002e-06 0.6001579761505127\n", "[LibSVM]6 1.0000000000000002e-07 4.574748277664185\n", "[LibSVM]7 1.0000000000000002e-08 0.6381776332855225\n", "[LibSVM]8 1.0000000000000003e-09 0.6316916942596436\n", "[LibSVM]9 1.0000000000000003e-10 0.6176648139953613\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import time\n", "\n", "tol = 0.1\n", "tols = []\n", "times = []\n", "for i in range(10):\n", " svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n", " t1 = time.time()\n", " svm_clf.fit(X, y)\n", " t2 = time.time()\n", " times.append(t2-t1)\n", " tols.append(tol)\n", " print(i, tol, t2-t1)\n", " tol /= 10\n", "plt.rcParams['font.family'] = 'stixgeneral'\n", "plt.semilogx(tols, times)\n", "plt.rcParams['font.family'] = 'NanumBarunGothic'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 배치 경사 하강법을 사용한 선형 SVM 분류기 구현" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# 훈련 세트\n", "X = iris[\"data\"][:, (2, 3)] # 꽃잎 길이, 꽃잎 너비\n", "y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1, 1) # Iris-Virginica" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1.],\n", " [0.]])" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.base import BaseEstimator\n", "\n", "class MyLinearSVC(BaseEstimator):\n", " def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n", " self.C = C\n", " self.eta0 = eta0\n", " self.n_epochs = n_epochs\n", " self.random_state = random_state\n", " self.eta_d = eta_d\n", "\n", " def eta(self, epoch):\n", " return self.eta0 / (epoch + self.eta_d)\n", " \n", " def fit(self, X, y):\n", " # Random initialization\n", " if self.random_state:\n", " np.random.seed(self.random_state)\n", " w = np.random.randn(X.shape[1], 1) # n feature weights\n", " b = 0\n", "\n", " m = len(X)\n", " t = y * 2 - 1 # -1 if t==0, +1 if t==1\n", " X_t = X * t\n", " self.Js=[]\n", "\n", " # Training\n", " for epoch in range(self.n_epochs):\n", " support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n", " X_t_sv = X_t[support_vectors_idx]\n", " t_sv = t[support_vectors_idx]\n", "\n", " J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\n", " self.Js.append(J)\n", "\n", " w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\n", " b_derivative = -C * np.sum(t_sv)\n", " \n", " w = w - self.eta(epoch) * w_gradient_vector\n", " b = b - self.eta(epoch) * b_derivative\n", " \n", "\n", " self.intercept_ = np.array([b])\n", " self.coef_ = np.array([w])\n", " support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n", " self.support_vectors_ = X[support_vectors_idx]\n", " return self\n", "\n", " def decision_function(self, X):\n", " return X.dot(self.coef_[0]) + self.intercept_[0]\n", "\n", " def predict(self, X):\n", " return (self.decision_function(X) >= 0).astype(np.float64)\n", "\n", "C=2\n", "svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\n", "svm_clf.fit(X, y)\n", "svm_clf.predict(np.array([[5, 2], [4, 1]]))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 60000, 0, 100]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n", "plt.axis([0, svm_clf.n_epochs, 0, 100])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-15.56761653] [[[2.28120287]\n", " [2.71621742]]]\n" ] } ], "source": [ "print(svm_clf.intercept_, svm_clf.coef_)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-15.51721253] [[2.27128546 2.71287145]]\n" ] } ], "source": [ "svm_clf2 = SVC(kernel=\"linear\", C=C)\n", "svm_clf2.fit(X, y.ravel())\n", "print(svm_clf2.intercept_, svm_clf2.coef_)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[4, 6, 0.8, 2.8]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yr = y.ravel()\n", "plt.figure(figsize=(12,3.2))\n", "plt.subplot(121)\n", "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\", label=\"Iris-Virginica\")\n", "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\", label=\"Not Iris-Virginica\")\n", "plot_svc_decision_boundary(svm_clf, 4, 6)\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.ylabel(\"꽃잎 너비\", fontsize=14)\n", "plt.title(\"MyLinearSVC\", fontsize=14)\n", "plt.axis([4, 6, 0.8, 2.8])\n", "\n", "plt.subplot(122)\n", "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n", "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n", "plot_svc_decision_boundary(svm_clf2, 4, 6)\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.title(\"SVC\", fontsize=14)\n", "plt.axis([4, 6, 0.8, 2.8])\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-12.52988101 1.94162342 1.84544824]\n" ] }, { "data": { "text/plain": [ "[4, 6, 0.8, 2.8]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.linear_model import SGDClassifier\n", "\n", "sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, tol=1e-3, max_iter = 50, random_state=42)\n", "sgd_clf.fit(X, y.ravel())\n", "\n", "m = len(X)\n", "t = y * 2 - 1 # -1 if t==0, +1 if t==1\n", "X_b = np.c_[np.ones((m, 1)), X] # 편향 x0=1을 추가\n", "X_b_t = X_b * t\n", "sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n", "print(sgd_theta)\n", "support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n", "sgd_clf.support_vectors_ = X[support_vectors_idx]\n", "sgd_clf.C = C\n", "\n", "plt.figure(figsize=(5.5,3.2))\n", "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n", "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n", "plot_svc_decision_boundary(sgd_clf, 4, 6)\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.ylabel(\"꽃잎 너비\", fontsize=14)\n", "plt.title(\"SGDClassifier\", fontsize=14)\n", "plt.axis([4, 6, 0.8, 2.8])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 연습문제 해답" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. to 7." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "부록 A 참조." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 8." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_문제: 선형적으로 분리되는 데이터셋에 `LinearSVC`를 훈련시켜보세요. 그런 다음 같은 데이터셋에 `SVC`와`SGDClassifier`를 적용해보세요. 거의 비슷한 모델이 만들어지는지 확인해보세요._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Iris 데이터셋을 사용하겠습니다. Iris Setosa와 Iris Versicolor 클래스는 선형적으로 구분이 가능합니다." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "from sklearn import datasets\n", "\n", "iris = datasets.load_iris()\n", "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n", "y = iris[\"target\"]\n", "\n", "setosa_or_versicolor = (y == 0) | (y == 1)\n", "X = X[setosa_or_versicolor]\n", "y = y[setosa_or_versicolor]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LinearSVC: [0.28474272] [[1.05364736 1.09903308]]\n", "SVC: [0.31896852] [[1.1203284 1.02625193]]\n", "SGDClassifier(alpha=0.00200): [0.117] [[0.77714169 0.72981762]]\n" ] } ], "source": [ "from sklearn.svm import SVC, LinearSVC\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "C = 5\n", "alpha = 1 / (C * len(X))\n", "\n", "lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n", "svm_clf = SVC(kernel=\"linear\", C=C)\n", "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, tol=1e-3, alpha=alpha,\n", " max_iter=100000, random_state=42)\n", "\n", "scaler = StandardScaler()\n", "X_scaled = scaler.fit_transform(X)\n", "\n", "lin_clf.fit(X_scaled, y)\n", "svm_clf.fit(X_scaled, y)\n", "sgd_clf.fit(X_scaled, y)\n", "\n", "print(\"LinearSVC: \", lin_clf.intercept_, lin_clf.coef_)\n", "print(\"SVC: \", svm_clf.intercept_, svm_clf.coef_)\n", "print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 세개 모델의 결정 경계를 그려 보겠습니다:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# 각 결정 경계의 기울기와 편향을 계산합니다\n", "w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\n", "b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\n", "w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\n", "b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\n", "w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\n", "b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\n", "\n", "# 결정 경계를 원본 스케일로 변환합니다\n", "line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\n", "line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\n", "line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\n", "\n", "# 세 개의 결정 경계를 모두 그립니다\n", "plt.figure(figsize=(11, 4))\n", "plt.plot(line1[:, 0], line1[:, 1], \"k:\", label=\"LinearSVC\")\n", "plt.plot(line2[:, 0], line2[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n", "plt.plot(line3[:, 0], line3[:, 1], \"r-\", label=\"SGDClassifier\")\n", "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris-Versicolor\"\n", "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris-Setosa\"\n", "plt.xlabel(\"꽃잎 길이\", fontsize=14)\n", "plt.ylabel(\"꽃잎 너비\", fontsize=14)\n", "plt.legend(loc=\"upper center\", fontsize=14)\n", "plt.axis([0, 5.5, 0, 2])\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "아주 비슷하네요!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 9." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_문제: MNIST 데이터셋에 SVM 분류기를 훈련시켜보세요. SVM 분류기는 이진 분류기라서 OvA 전략을 사용해 10개의 숫자를 분류해야 합니다. 처리 속도를 높이기 위해 작은 검증 세트로 하이퍼파라미터를 조정하는 것이 좋습니다. 어느 정도까지 정확도를 높일 수 있나요?_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "먼저 데이터셋을 로드하고 훈련 세트와 테스트 세트로 나눕니다. `train_test_split()` 함수를 사용할 수 있지만 보통 처음 60,000개의 샘플을 훈련 세트로 사용하고 나머지는 10,000개를 테스트 세트로 사용합니다(이렇게 하면 다른 사람들의 모델과 성능을 비교하기 좋습니다):" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# from sklearn.datasets import fetch_mldata\n", "\n", "# mnist = fetch_mldata(\"MNIST original\")\n", "from sklearn.datasets import fetch_openml\n", "mnist = fetch_openml('mnist_784', version=1)\n", "X = mnist[\"data\"]\n", "y = mnist[\"target\"]\n", "\n", "X_train = X[:60000]\n", "y_train = y[:60000]\n", "X_test = X[60000:]\n", "y_test = y[60000:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "많은 훈련 알고리즘은 훈련 샘플의 순서에 민감하므로 먼저 이를 섞는 것이 좋은 습관입니다:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "np.random.seed(42)\n", "rnd_idx = np.random.permutation(60000)\n", "X_train = X_train[rnd_idx]\n", "y_train = y_train[rnd_idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "선형 SVM 분류기부터 시작해보죠. 이 모델은 자동으로 OvA(또는 OvR) 전략을 사용하므로 특별히 처리해 줄 것이 없습니다. 간단하네요!" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/haesun/anaconda3/envs/handson-ml/lib/python3.7/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " \"the number of iterations.\", ConvergenceWarning)\n" ] }, { "data": { "text/plain": [ "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=10000,\n", " multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n", " verbose=0)" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lin_clf = LinearSVC(max_iter=10000, random_state=42)\n", "lin_clf.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "훈련 세트에 대한 예측을 만들어 정확도를 측정해 보겠습니다(최종 모델을 선택해 훈련시킨 것이 아니기 때문에 아직 테스트 세트를 사용해서는 안됩니다):" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.86765" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", "y_pred = lin_clf.predict(X_train)\n", "accuracy_score(y_train, y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "와우, MNIST에서 86% 정확도면 나쁜 성능입니다. 선형 모델이 MNIST 문제에 너무 단순하기 때문이지만 먼저 데이터의 스케일을 조정할 필요가 있습니다:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n", "X_test_scaled = scaler.transform(X_test.astype(np.float32))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/haesun/anaconda3/envs/handson-ml/lib/python3.7/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " \"the number of iterations.\", ConvergenceWarning)\n" ] }, { "data": { "text/plain": [ "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=10000,\n", " multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n", " verbose=0)" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lin_clf = LinearSVC(max_iter=10000, random_state=42)\n", "lin_clf.fit(X_train_scaled, y_train)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9274166666666667" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = lin_clf.predict(X_train_scaled)\n", "accuracy_score(y_train, y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "훨씬 나아졌지만(에러율을 절반으로 줄였습니다) 여전히 MNIST에서 좋은 성능은 아닙니다. SVM을 사용한다면 커널 함수를 사용해야 합니다. RBF 커널(기본값)로 `SVC`를 적용해 보겠습니다.\n", "\n", "**경고**: 사이킷런 0.19버전 이하를 사용하면 기본적으로 OvO 전략을 사용할 것이므로 `decision_function_shape=\"ovr\"`로 지정해 OvR 전략으로 바꾸어 주어야 합니다(OvR은 0.19 버전부터 기본이 되었습니다)." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svm_clf = SVC(gamma='auto', decision_function_shape=\"ovr\")\n", "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9476" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = svm_clf.predict(X_train_scaled)\n", "accuracy_score(y_train, y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "아주 좋네요 6배나 적은 데이터에서 모델을 훈련시켰지만 더 좋은 성능을 얻었습니다. 교차 검증을 사용한 랜덤 서치로 하이퍼파라미터 튜닝을 해보겠습니다. 진행을 빠르게 하기 위해 작은 데이터셋으로 작업하겠습니다:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 7.7s finished\n" ] }, { "data": { "text/plain": [ "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n", " estimator=SVC(C=1.0, cache_size=200, class_weight=None,\n", " coef0=0.0, decision_function_shape='ovr',\n", " degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False,\n", " random_state=None, shrinking=True, tol=0.001,\n", " verbose=False),\n", " iid='warn', n_iter=10, n_jobs=-1,\n", " param_distributions={'C': ,\n", " 'gamma': },\n", " pre_dispatch='2*n_jobs', random_state=None, refit=True,\n", " return_train_score=False, scoring=None, verbose=2)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import RandomizedSearchCV\n", "from scipy.stats import reciprocal, uniform\n", "\n", "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n", "rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, cv=3, n_iter=10, verbose=2, n_jobs=-1)\n", "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n", " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", " shrinking=True, tol=0.001, verbose=False)" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd_search_cv.best_estimator_" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.864" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd_search_cv.best_score_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이 점수는 낮지만 1,000개의 샘플만 사용한 것을 기억해야 합니다. 전체 데이터셋으로 최선의 모델을 재훈련시켜 보겠습니다(몇 시간이 걸릴지 모르니 퇴근하기 전에 돌려 보세요):" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "SVC(C=8.852316058423087, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n", " kernel='rbf', max_iter=-1, probability=False, random_state=None,\n", " shrinking=True, tol=0.001, verbose=False)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.99965" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n", "accuracy_score(y_train, y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "아주 훌륭하네요! 이 모델을 선택하겠습니다. 이제 테스트 세트로 모델을 테스트합니다:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9709" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n", "accuracy_score(y_test, y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "아주 나쁘지 않지만 확실히 모델이 다소 과대적합되었습니다. 하이퍼파라미터를 조금 더 수정할 수 있지만(가령, `C`와/나 `gamma`를 감소시킵니다) 그렇게 하면 테스트 세트에 과대적합될 위험이 있습니다. 다른 사람들은 하이퍼파라미터 `C=5`와 `gamma=0.005`에서 더 나은 성능(98% 이상의 정확도)을 얻었습니다. 훈련 세트를 더 많이 사용해서 더 오래 랜덤 서치를 수행하면 이런 값을 얻을 수 있을지 모릅니다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 10." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_문제: 캘리포니아 주택 가격 데이터셋에 SVM 회귀를 훈련시켜보세요._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "사이킷런의 `fetch_california_housing()` 함수를 사용해 데이터셋을 로드합니다:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/haesun/anaconda3/envs/handson-ml/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n", " warnings.warn(msg, category=DeprecationWarning)\n" ] } ], "source": [ "from sklearn.datasets import fetch_california_housing\n", "\n", "housing = fetch_california_housing()\n", "X = housing[\"data\"]\n", "y = housing[\"target\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "훈련 세트와 테스트 세트로 나눕니다:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "데이터의 스케일을 조정하는 것을 잊지 마세요:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "\n", "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "먼저 간단한 `LinearSVR`을 훈련시켜 보죠:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n", " intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=10000,\n", " random_state=42, tol=0.0001, verbose=0)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import LinearSVR\n", "\n", "lin_svr = LinearSVR(max_iter=10000, random_state=42)\n", "lin_svr.fit(X_train_scaled, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "훈련 세트에 대한 성능을 확인해 보겠습니다:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9567664024747589" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import mean_squared_error\n", "\n", "y_pred = lin_svr.predict(X_train_scaled)\n", "mse = mean_squared_error(y_train, y_pred)\n", "mse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "RMSE를 확인해 보겠습니다:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9781443668880166" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sqrt(mse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "훈련 세트에서 타깃은 만달러 단위입니다. RMSE는 기대할 수 있는 에러의 정도를 대략 가늠하게 도와줍니다(에러가 클수록 큰 폭으로 증가합니다). 이 모델의 에러가 대략 $10,000 정도로 예상할 수 있습니다. 썩 훌륭하지 않네요. RBF 커널이 더 나을지 확인해 보겠습니다. 하이퍼파라미터 `C`와 `gamma`의 적절한 값을 찾기 위해 교차 검증을 사용한 랜덤 서치를 적용하겠습니다:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 40.8s finished\n" ] }, { "data": { "text/plain": [ "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n", " estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n", " epsilon=0.1, gamma='auto_deprecated',\n", " kernel='rbf', max_iter=-1, shrinking=True,\n", " tol=0.001, verbose=False),\n", " iid='warn', n_iter=10, n_jobs=-1,\n", " param_distributions={'C': ,\n", " 'gamma': },\n", " pre_dispatch='2*n_jobs', random_state=42, refit=True,\n", " return_train_score=False, scoring=None, verbose=2)" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.svm import SVR\n", "from sklearn.model_selection import RandomizedSearchCV\n", "from scipy.stats import reciprocal, uniform\n", "\n", "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n", "rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, cv=3, n_iter=10, verbose=2, random_state=42, n_jobs=-1)\n", "rnd_search_cv.fit(X_train_scaled, y_train)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SVR(C=4.745401188473625, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n", " gamma=0.07969454818643928, kernel='rbf', max_iter=-1, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rnd_search_cv.best_estimator_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "이제 훈련 세트에서 RMSE를 측정해 보겠습니다:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5727524770785356" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n", "mse = mean_squared_error(y_train, y_pred)\n", "np.sqrt(mse)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "선형 모델보다 훨씬 나아졌네요. 이 모델을 선택하고 테스트 세트에서 평가해 보겠습니다:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.592916838552874" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n", "mse = mean_squared_error(y_test, y_pred)\n", "np.sqrt(mse)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": {}, "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 }