{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Linearní regrese\n", "\n", "http://scikit-learn.org/stable/index.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bias / variance" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [], "source": [ "np.random.seed(40)\n", "Nsampl = 100\n", "Xmax = 10\n", "learn = np.s_[:40]\n", "valid = np.s_[40:80]\n", "test = np.s_[80:]\n", "X = np.random.random(Nsampl)*Xmax\n", "Y = np.sin(X) + np.random.normal(size = X.shape)*.7" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "data": { "image/png": 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9zW92WeZZpZLsZjMDZ63jy2WPMb+HjrdlFTcPTGXa3G20JcXi/+hTRF98vS+6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\nJYQQbTLYYoltDCG+RyolJZVo7d/maR0qqZVSy4HWa3wVoIE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X[learn], Y[learn], \".\", color=\"0.8\")\n", "plt.plot(X[valid], Y[valid], \".\")\n", "plt.plot(X[test], Y[test], \".\")\n", "err = []\n", "for deg in range(16):\n", " poly = np.polyfit(X[learn], Y[learn], deg)\n", " x = np.linspace(0, Xmax)\n", " plt.plot(x, np.polyval(poly, x))\n", " sqerr = (np.polyval(poly, X) - Y)**2\n", " err.append([deg, np.mean(sqerr[learn]), np.mean(sqerr[valid]), np.mean(sqerr[test])])\n", "err = np.array(err)" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ISJkyIn/9lf1jxMaK/PCDSJMmInXrinz9tcilS2n3i48XadVK5NNPXR7moYdE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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(err[:,0], err[:,1], label=\"learn\")\n", "plt.plot(err[:,0], err[:,2], label=\"valid\")\n", "plt.plot(err[:,0], err[:,3], label=\"test\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Jiný příklad" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [], "source": [ "# http://www.sidc.be/silso/DATA/yearssn.dat\n", "data = np.loadtxt(\"data/yearssn.dat\")" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GOjmp9p5auJBXBAMdQgAUBXD//cDf/m2x9ckDUwG4Zsm68QtR3iBwVgsopQLQO6mWbQV1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\nKWIAQsw95MZEPQQgRC0OEBuo9DXNwGG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(data[:,0], data[:,1])" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": [ "# http://glowingpython.blogspot.cz/2015/01/forecasting-beer-consumption-with.html\n", "def organize_data(to_forecast, window, horizon):\n", " \"\"\"\n", " Input:\n", " to_forecast, univariate time series organized as numpy array\n", " window, number of items to use in the forecast window\n", " horizon, horizon of the forecast\n", " Output:\n", " X, a matrix where each row contains a forecast window\n", " y, the target values for each row of X\n", " \"\"\"\n", " shape = to_forecast.shape[:-1] + \\\n", " (to_forecast.shape[-1] - window + 1, window)\n", " strides = to_forecast.strides + (to_forecast.strides[-1],)\n", " X = np.lib.stride_tricks.as_strided(to_forecast, \n", " shape=shape, \n", " strides=strides)\n", " y = np.array([X[i+horizon][-1] for i in range(len(X)-horizon)])\n", " return X[:-horizon], y\n", "\n", "k = 20 # number of previous observations to use\n", "h = 7 # forecast horizon\n", "X,y = organize_data(data[:,1], k, h)" ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n", " weights='distance')" ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression, ElasticNet\n", "from sklearn.neighbors import KNeighborsRegressor\n", "\n", "\n", "m = k+40 # number of samples to take in account\n", "regressor = LinearRegression(normalize=True)\n", "regressor = ElasticNet(normalize=True, alpha=0.01)\n", "regressor = ElasticNet(normalize=True, alpha=0.001, positive=True)\n", "regressor = KNeighborsRegressor(1, weights=\"distance\")\n", "regressor.fit(X[:m], y[:m])" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The error is 2611.80\n" ] } ], "source": [ "def mape(ypred, ytrue):\n", " \"\"\" returns the mean absolute percentage error \"\"\"\n", " idx = ytrue != 0.0\n", " return 100*np.mean(np.abs(ypred[idx]-ytrue[idx]))\n", " return 100*np.mean(np.abs(ypred[idx]-ytrue[idx])/ytrue[idx])\n", "\n", "print('The error is %0.2f' % mape(regressor.predict(X[m:]),y[m:]))" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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E5iezGVL3IAMLLauVW8wFIhJceskFV7JMcvEicPnycudDLJ5YcH33d+cUXCLq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\nYd93UUkILk1jYH4DV26ku6sMJYehTZ9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caRyuuDR23AbSMbME18u3Xo6qEV1j8pzqggDQ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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,6))\n", "plt.plot(y)\n", "eps=1e-3\n", "plt.plot(regressor.predict(X+eps))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "http://www.kurzweilai.net/machine-learning-helps-stanford-physicists-predict-dangerous-solar-flares-earlier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Neparametrické modely" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AX9buW3PeUb+DzXWbGdJzSLOyX+z4gsHZgyMPXkSiQslZIqaac3RFY2KT3t16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\nJAolZxERjzqFSaJQchYR8aj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7B/jCObcQ1ZojcZJzbhxwJnCdd+kvplIxOa8DhgUtD8Nf\nexbpUGaWCfwV+INz7oV4x5PMnHNbgb8Dx8Y7liQzHjjPu2b6LHC6mT0V55iSjnNug/fvl0AJ/sun\nMZWKyfl9YLSZjTSzLsDFwJw4xySdjJkZMBtY6px7MN7xJCMz62dmvb3nPiAfWBjfqJKLc+4W59ww\n59wo4DvA6865y+IdVzIxs+5mlu09zwIKgJjfyZJyydk51wBcD5QCS4E/qYfsgTGzZ4F/AmPMbI2Z\nfT/eMSWhk4DvAl/3br9YaGYT4h1UkhkEvO5dc34XeNE591qcY0p2uux34HKBN4O+hy8558pivdOU\nu5VKREQk2aVczVlERCTZKTmLiIgkGCVnERGRBKPkLCIikmCUnEVERBKMkrOIiEiCUXIWERFJMErO\nIiIiCeb/A6xNhvsH+kFEAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn import neighbors\n", "# http://scikit-learn.org/stable/auto_examples/neighbors/plot_regression.html\n", "np.random.seed(0)\n", "X = np.sort(5 * np.random.rand(40, 1), axis=0)\n", "T = np.linspace(0, 5, 500)[:, np.newaxis]\n", "y = np.sin(X).ravel()\n", "\n", "# Add noise to targets\n", "y[::5] += 1 * (0.5 - np.random.rand(8))\n", "\n", "###############################################################################\n", "# Fit regression model\n", "n_neighbors = 5\n", "\n", "figure(figsize=(8,5))\n", "\n", "knn = neighbors.KNeighborsRegressor(n_neighbors, weights=\"uniform\")\n", "y_ = knn.fit(X, y).predict(T)\n", "\n", "plt.scatter(X, y, c='k', label='data')\n", "plt.plot(T, y_, c='g', label='prediction')\n", "plt.axis('tight')\n", "plt.legend()\n", "plt.title(\"KNeighborsRegressor (k = %i, weights = uniform)\" % (n_neighbors,))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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m4cKF9q2qJyXmRYsW8corr/DFF1+Ql5dHbm4uISEhHnMLWW1IchZ1lleUh8nH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\naA9ZBVlt+prJqs+ryC7MZsEdTc9qNjwmeCdMCQS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure(figsize=(8,5))\n", "\n", "knn = neighbors.KNeighborsRegressor(n_neighbors, weights=\"distance\")\n", "y_ = knn.fit(X, y).predict(T)\n", "\n", "plt.scatter(X, y, c='k', label='data')\n", "plt.plot(T, y_, c='g', label='prediction')\n", "plt.axis('tight')\n", "plt.legend()\n", "plt.title(\"KNeighborsRegressor (k = %i, weights = distance)\" % (n_neighbors,))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Kernel density estimation\n", "jádrový odhad hustoty" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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XX345KSkprFixIujH79GjB0ePHi30vJiK4ddff2XgwIE0btyYli1b0qZNG+8M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\nBPTRo0fTrdu9VK1arcxttG3bnf37a/Dhh0eLeK0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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import norm\n", "from sklearn.neighbors import KernelDensity\n", "\n", "\n", "#----------------------------------------------------------------------\n", "# Plot the progression of histograms to kernels\n", "np.random.seed(1)\n", "N = 20\n", "X = np.concatenate((np.random.normal(0, 1, 0.3 * N),\n", " np.random.normal(5, 1, 0.7 * N)))[:, np.newaxis]\n", "X_plot = np.linspace(-5, 10, 1000)[:, np.newaxis]\n", "bins = np.linspace(-5, 10, 10)\n", "\n", "fig, ax = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(8,5))\n", "fig.subplots_adjust(hspace=0.05, wspace=0.05)\n", "\n", "# histogram 1\n", "ax[0, 0].hist(X[:, 0], bins=bins, fc='#AAAAFF', normed=True)\n", "ax[0, 0].text(-3.5, 0.31, \"Histogram\")\n", "\n", "# histogram 2\n", "ax[0, 1].hist(X[:, 0], bins=bins + 0.75, fc='#AAAAFF', normed=True)\n", "ax[0, 1].text(-3.5, 0.31, \"Histogram, bins shifted\")\n", "\n", "# tophat KDE\n", "kde = KernelDensity(kernel='tophat', bandwidth=0.75).fit(X)\n", "log_dens = kde.score_samples(X_plot)\n", "ax[1, 0].fill(X_plot[:, 0], np.exp(log_dens), fc='#AAAAFF')\n", "ax[1, 0].text(-3.5, 0.31, \"Tophat Kernel Density\")\n", "\n", "# Gaussian KDE\n", "kde = KernelDensity(kernel='gaussian', bandwidth=0.75).fit(X)\n", "log_dens = kde.score_samples(X_plot)\n", "ax[1, 1].fill(X_plot[:, 0], np.exp(log_dens), fc='#AAAAFF')\n", "ax[1, 1].text(-3.5, 0.31, \"Gaussian Kernel Density\")\n", "\n", "for axi in ax.ravel():\n", " axi.plot(X[:, 0], np.zeros(X.shape[0]) - 0.01, '+k')\n", " axi.set_xlim(-4, 9)\n", " axi.set_ylim(-0.02, 0.34)\n", "\n", "for axi in ax[:, 0]:\n", " axi.set_ylabel('Normalized Density')\n", "\n", "for axi in ax[1, :]:\n", " axi.set_xlabel('x')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Klasifikace\n", "\n", "## k-nearest neighbor" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6kJfXp+L5zJnja1w24nW+ZnYYUOqc22VmzYBXgfHOuTfCljkC2Oqcc2Z2BjDd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\nfr9iwIDfxSjI6DR0/xFJVRGTr3NuM7A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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.colors import ListedColormap\n", "from sklearn import datasets\n", "\n", "n_neighbors = 15\n", "\n", "# import some data to play with\n", "iris = datasets.load_iris()\n", "X = iris.data[:, :2] # we only take the first two features. We could\n", " # avoid this ugly slicing by using a two-dim dataset\n", "y = iris.target\n", "\n", "h = .02 # step size in the mesh\n", "\n", "# Create color maps\n", "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n", "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n", "\n", "weights = 'distance'\n", "# we create an instance of Neighbours Classifier and fit the data.\n", "clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n", "clf.fit(X, y)\n", "\n", "# Plot the decision boundary. For that, we will assign a color to each\n", "# point in the mesh [x_min, m_max]x[y_min, y_max].\n", "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", "# Put the result into a color plot\n", "Z = Z.reshape(xx.shape)\n", "plt.figure(figsize=(8,7))\n", "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n", "\n", "# Plot also the training points\n", "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n", "plt.xlim(xx.min(), xx.max())\n", "plt.ylim(yy.min(), yy.max())\n", "plt.title(\"3-Class classification (k = %i, weights = '%s')\"\n", " % (n_neighbors, weights))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Naive Bayes" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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LiMXmkMs/H1MoEokxceIJVKv2eU3A8eOLsX37XxAIBHBwKIOpU/0y3RecUykp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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#http://www.astroml.org/book_figures/chapter9/fig_simple_naivebayes.html\n", "from matplotlib import colors\n", "\n", "from sklearn.naive_bayes import GaussianNB\n", "\n", "#------------------------------------------------------------\n", "# Simulate some data\n", "np.random.seed(0)\n", "mu1 = [1, 1]\n", "cov1 = 0.6 * np.eye(2)\n", "\n", "mu2 = [5, 3]\n", "cov2 = np.eye(2) * np.array([1.0, 0.1])\n", "\n", "X = np.concatenate([np.random.multivariate_normal(mu1, cov1, 100),\n", " np.random.multivariate_normal(mu2, cov2, 100)])\n", "y = np.zeros(200)\n", "y[100:] = 1\n", "\n", "#------------------------------------------------------------\n", "# Fit the Naive Bayes classifier\n", "clf = GaussianNB()\n", "clf.fit(X, y)\n", "\n", "# predict the classification probabilities on a grid\n", "xlim = (-1, 8)\n", "ylim = (-1, 5)\n", "xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 71),\n", " np.linspace(ylim[0], ylim[1], 81))\n", "Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n", "Z = Z[:, 1].reshape(xx.shape)\n", "\n", "#------------------------------------------------------------\n", "# Plot the results\n", "fig = plt.figure(figsize=(8, 7))\n", "ax = fig.add_subplot(111)\n", "ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.binary, zorder=2)\n", "\n", "ax.contour(xx, yy, Z, [0.5], colors='k')\n", "cmap_light2 = ListedColormap(['#FFAAAA', '#AAAAFF'])\n", "plt.pcolormesh(xx, yy, Z, cmap=cmap_light2)\n", "\n", "ax.set_xlim(xlim)\n", "ax.set_ylim(ylim)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision trees\n", "rozhodovací stromy" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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OOgGAABoNaDoBgAAaDWg6AYAAGg1oOgGAABoNaDoBgAAaDWg6AYAAGg1oOgGA\nABoNaDoBgAAaDWg6AYAAGg1oOgGAABoNaDoBgAAaDWg6AYAAGg1oOgGAABoNaDoBgAAaDWg6AYAA\nGg1oOgGAABoNaDoBgAAaDWg6AYAAGg1oOgGAABoNaDoBgAAaDWg6AYAAGg1oOgGAABoNaDoBgAAa\nDWg6AYAAGg1oOgGAABoNaDoBgAAaDWg6AYAAGg1oOgGAABoNaDoBgAAaDWg6AYAAGg1oOgGAABoN\naDoBgAAaDWg6AYAAGg1oOgGAAAMAmflvOK9Yn5AAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='data/CART_tree_titanic_survivors.png')\n", "#http://en.wikipedia.org/wiki/File:CART_tree_titanic_survivors.png" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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0rMbPd2NQDOHx3knEysr93FBTF2HojRq8w3GkX024/5Q7cRnvJIK8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UAAAg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\nLrlLXVFMi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "# Parameters\n", "n_classes = 3\n", "plot_colors = \"bry\"\n", "plot_step = 0.02\n", "\n", "# Load data\n", "iris = load_iris()\n", "\n", "fig = plt.figure(figsize=(10, 8))\n", "\n", "for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],\n", " [1, 2], [1, 3], [2, 3]]):\n", " # We only take the two corresponding features\n", " X = iris.data[:, pair]\n", " y = iris.target\n", "\n", " # Shuffle\n", " idx = np.arange(X.shape[0])\n", " np.random.seed(13)\n", " np.random.shuffle(idx)\n", " X = X[idx]\n", " y = y[idx]\n", "\n", " # Standardize\n", " mean = X.mean(axis=0)\n", " std = X.std(axis=0)\n", " X = (X - mean) / std\n", "\n", " # Train\n", " clf = DecisionTreeClassifier().fit(X, y)\n", "\n", " # Plot the decision boundary\n", " plt.subplot(2, 3, pairidx + 1)\n", "\n", " x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", " y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", " xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n", " np.arange(y_min, y_max, plot_step))\n", "\n", " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", " Z = Z.reshape(xx.shape)\n", " cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n", "\n", " plt.xlabel(iris.feature_names[pair[0]])\n", " plt.ylabel(iris.feature_names[pair[1]])\n", " plt.axis(\"tight\")\n", "\n", " # Plot the training points\n", " for i, color in zip(range(n_classes), plot_colors):\n", " idx = np.where(y == i)\n", " plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],\n", " cmap=plt.cm.Paired)\n", "\n", " plt.axis(\"tight\")\n", "\n", "plt.suptitle(\"Decision surface of a decision tree using paired features\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CIFSJ9H/o+QSOxc5jKKjahzJXoeS43iSR4mSMVnMKbInZeOz211DjLvFq05/2g8nIlGuF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\njRrr8HnOJpiWX6Thfe5+9PMj+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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.cluster import KMeans\n", "from sklearn.datasets.samples_generator import make_blobs\n", "\n", "##############################################################################\n", "# Generate sample data\n", "np.random.seed(0)\n", "\n", "batch_size = 45\n", "centers = [[1, 1], [-1, -1], [1, -1]]\n", "n_clusters = len(centers)\n", "X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7)\n", "\n", "##############################################################################\n", "# Compute clustering with Means\n", "\n", "k_means = KMeans(init='k-means++', n_clusters=3, n_init=10)\n", "k_means.fit(X)\n", "k_means_labels = k_means.labels_\n", "k_means_cluster_centers = k_means.cluster_centers_\n", "k_means_labels_unique = np.unique(k_means_labels)\n", "\n", "##############################################################################\n", "# Plot result\n", "\n", "fig = plt.figure(figsize=(8, 6))\n", "fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)\n", "colors = ['#4EACC5', '#FF9C34', '#4E9A06']\n", "\n", "ax = fig.add_subplot(111)\n", "for k, col in zip(range(n_clusters), colors):\n", " my_members = k_means_labels == k\n", " cluster_center = k_means_cluster_centers[k]\n", " ax.plot(X[my_members, 0], X[my_members, 1], 'w',\n", " markerfacecolor=col, marker='.')\n", " ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n", " markeredgecolor='k', markersize=6)\n", "ax.set_title('KMeans')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 1 }