├── requirements.txt ├── .gitignore ├── README.md └── ROC-Keras.ipynb /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | keras 3 | sklearn 4 | scipy 5 | matplotlib 6 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.hdf5 2 | *.ipynb_checkpoints 3 | *.p 4 | *.HDF5 5 | __pycache__ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # [Simple guide on how to generate ROC plot for Keras classifier](https://www.dlology.com/blog/simple-guide-on-how-to-generate-roc-plot-for-keras-classifier/) 2 | 3 | 4 | ## How to Run 5 | Require [Python 3.5+](https://www.python.org/ftp/python/3.6.4/python-3.6.4.exe) and [Jupyter notebook](https://jupyter.readthedocs.io/en/latest/install.html) installed 6 | ### Clone or download this repo 7 | ``` 8 | git clone https://github.com/Tony607/ROC-Keras 9 | ``` 10 | ### Install required libraries 11 | `pip3 install -r requirements.txt` 12 | 13 | 14 | In the project start a command line run 15 | ``` 16 | jupyter notebook 17 | ``` 18 | In the opened browser window open 19 | ``` 20 | ROC-Keras.ipynb 21 | ``` 22 | -------------------------------------------------------------------------------- /ROC-Keras.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Binary classification\n", 8 | "### Generate some train/test data" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "import numpy as np\n", 18 | "np.random.seed(10)\n", 19 | "\n", 20 | "import matplotlib.pyplot as plt\n", 21 | "\n", 22 | "from sklearn.datasets import make_classification\n", 23 | "from sklearn.ensemble import RandomForestClassifier\n", 24 | "from sklearn.model_selection import train_test_split\n", 25 | "from sklearn.metrics import roc_curve\n", 26 | "\n", 27 | "X, y = make_classification(n_samples=80000)\n", 28 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)\n", 29 | "\n", 30 | "X_train, X_train_lr, y_train, y_train_lr = train_test_split(X_train,\n", 31 | " y_train,\n", 32 | " test_size=0.5)" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "### Build and train a Keras classifier model as usual" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 2, 45 | "metadata": {}, 46 | "outputs": [ 47 | { 48 | "name": "stderr", 49 | "output_type": "stream", 50 | "text": [ 51 | "c:\\users\\hasee\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", 52 | " from ._conv import register_converters as _register_converters\n", 53 | "Using TensorFlow backend.\n" 54 | ] 55 | }, 56 | { 57 | "name": "stdout", 58 | "output_type": "stream", 59 | "text": [ 60 | "Epoch 1/5\n", 61 | "20000/20000 [==============================] - 6s 278us/step - loss: 0.3550 - acc: 0.8667\n", 62 | "Epoch 2/5\n", 63 | "20000/20000 [==============================] - 1s 39us/step - loss: 0.1996 - acc: 0.9346\n", 64 | "Epoch 3/5\n", 65 | "20000/20000 [==============================] - 1s 35us/step - loss: 0.1853 - acc: 0.9393\n", 66 | "Epoch 4/5\n", 67 | "20000/20000 [==============================] - 1s 37us/step - loss: 0.1756 - acc: 0.9431\n", 68 | "Epoch 5/5\n", 69 | "20000/20000 [==============================] - 1s 38us/step - loss: 0.1659 - acc: 0.9480\n" 70 | ] 71 | }, 72 | { 73 | "data": { 74 | "text/plain": [ 75 | "" 76 | ] 77 | }, 78 | "execution_count": 2, 79 | "metadata": {}, 80 | "output_type": "execute_result" 81 | } 82 | ], 83 | "source": [ 84 | "from keras.models import Sequential\n", 85 | "from keras.layers import Dense\n", 86 | "\n", 87 | "def build_model():\n", 88 | " model = Sequential()\n", 89 | " model.add(Dense(20, input_dim=20, activation='relu'))\n", 90 | " model.add(Dense(40, activation='relu'))\n", 91 | " model.add(Dense(1, activation='sigmoid'))\n", 92 | " # Compile model\n", 93 | " model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", 94 | " return model\n", 95 | "\n", 96 | "from keras.wrappers.scikit_learn import KerasClassifier\n", 97 | "keras_model = build_model()\n", 98 | "keras_model.fit(X_train, y_train, epochs=5, batch_size=100, verbose=1)" 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "metadata": {}, 104 | "source": [ 105 | "### Use trained Keras model to predict test inputs and generate ROC data\n", 106 | "* fpr: False positive rate\n", 107 | "* tpr: True positive rate\n", 108 | "\n", 109 | "False Positive Rate$$\n", 110 | "FPR=\\frac{FP}\n", 111 | "{N}=\\frac{FP}\n", 112 | "{FP+TN}\\\\\n", 113 | "$$\n", 114 | "Where FP is the number of false positives, TN is the number of true negatives and N=FP+TN is the total number of negatives.\n", 115 | "True Positive Rate$$\n", 116 | "TPR=\\frac{TP}\n", 117 | "{P}=\\frac{TP}\n", 118 | "{FN+TP}\\\\\n", 119 | "$$\n", 120 | "Where TP is the number of true positives, FN is the number of false negatives and P=FN+TP is the total number of positives.\n" 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": 3, 126 | "metadata": {}, 127 | "outputs": [], 128 | "source": [ 129 | "y_pred_keras = keras_model.predict(X_test).ravel()\n", 130 | "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred_keras)" 131 | ] 132 | }, 133 | { 134 | "cell_type": "markdown", 135 | "metadata": {}, 136 | "source": [ 137 | "### Calculate AUC (area under curve)" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": 4, 143 | "metadata": {}, 144 | "outputs": [], 145 | "source": [ 146 | "from sklearn.metrics import auc\n", 147 | "auc_keras = auc(fpr_keras, tpr_keras)" 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": {}, 153 | "source": [ 154 | "## Train another classifier to compare with Keras model" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 5, 160 | "metadata": {}, 161 | "outputs": [], 162 | "source": [ 163 | "# Supervised transformation based on random forests\n", 164 | "rf = RandomForestClassifier(max_depth=3, n_estimators=10)\n", 165 | "rf.fit(X_train, y_train)\n", 166 | "\n", 167 | "y_pred_rf = rf.predict_proba(X_test)[:, 1]\n", 168 | "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, y_pred_rf)\n", 169 | "auc_rf = auc(fpr_rf, tpr_rf)" 170 | ] 171 | }, 172 | { 173 | "cell_type": "markdown", 174 | "metadata": {}, 175 | "source": [ 176 | "### Plot the ROC curve" 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": 6, 182 | "metadata": {}, 183 | "outputs": [ 184 | { 185 | "data": { 186 | "image/png": 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UTsdJDCtEZJGI/CAi5XL4KKD6W5kLu56CqaAOHjzIiBEjOP3008nOzubjjz9m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\n9aL+ZoAFGkYbGztzCpRsT8Hn89G/f3/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zszWnmF35pazbWUB2YRkClHsP21dl5ZHcLppN2UUUl9cclxYVIaR3TeTYAakU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\nSFUdCUwGNqrq0qDzflxxXFV3hvAd6hU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SWVX3lkrano+lhfhSy3OkOkqaJ2m1pK1KbqsW\nFu4tk/RsoSznNaJjI3rme7sLuqwoxM/M9WN7ri8T2qWnpBOr6uabkj6R77WjPL8kaVv+2z4s6b2F\ne+XVzYgYNQdp8cAzwOHABGAjMLsqzeeBG3J4EXB3Ds/O6ScCM7OcsfXIbLGeRwPvzuGjgOcLeR4B\n5u8l5TkD2DKA3LXAcaTvrX4AfLRdelalmQvsbHN5zgB+C7gNOKsQfzCwM58PyuGDyi7PBnX8ADAr\nh98NvAgcmK+XFdO2syzzvdcHkLscWJTDNwCfa6eeVX//V4F3tbE8Tyw8/3P0/a+XWjdHW0+nHjc4\nZwC35vA/ASdn69t01zpl6BkR6yOi8u3RVqBL0sQG9Sldz4EESjoMmBwRqyPVytuAT+wlep4N3Nmg\nLg3pGRHPRcQmoLcq70eAhyLi1Yj4H+Ah4LQmlOeIdYyIH0fE9hx+AfhvYEoDujRFz4HI9eEkUv2A\nVF+aXjfr1PMs4AcR8YsG9WlEz1WF568hfQcJJdfN0WZ0arnWmTpQmojoAf4P+PVB8tYjs5V6FjkT\nWB8RbxXibsnd7SsbHWYpQc+ZSh7C/13S8YX0u4aQ2Wo9Kyykv9FpdXkON2/Z5VlKfZd0LOmN+ZlC\n9F/koZnvlPCi1KieXZK6Ja2pDFmR6sP/5voxEpnN0LPCIvrXzXaW5/mknstgeUdUN0eb0anHtc5A\naYYb3wiN6JluSnOAa4DPFu4vjoi5wPH5OKeNer4ITI/kIfxLwB2SJtcpc7iUUZ4fAn4REUUffO0o\nz+HmLbs8G5aX33BvBz4dEZW398uA3wQ+SBqG+UoDOkLjek6P5Gbmj4HvSnpfCTJrUVZ5ziV9f1ih\nbeUpaQkwH/j2EHlH9NtHm9Gpx7XOO2kkjSN5r351kLz1yGylnkiaBtwHnBsR77xJRsTz+fwacAep\ny9wWPfMw5StZnydIb7wfyOmnFfK3vTwz/d4k21Sew81bdnk2VN/zi8X3gSsi4h2nvBHxYiTeAm6h\nvWVZGf4jInaS5u6OJjnYPDDXj2HLbIaemU8B90XEryoR7SpPSacAlwOnF0ZYyq2bZU1UteIgeVDY\nSVoIUJkMm1OV5gvsOaG8PIfnsOdCgp2kybUhZbZYzwNz+jNryDwkh8eTxqUvbKOeU4CxOXw48Dxw\ncL5eB/wOfZOLC9qlZ74eQ/oHObzd5VlIu4z+CwmeJU3UHpTDpZdngzpOAB4G/rxG2sPyWcB3gW+2\nsSwPAibm8CHAdvKkOXAPey4k+Hy79CzErwFObHd5kgzzM+TFIs2qmyP+Ee06gAXAj3PhXJ7jriZZ\nZkj+2+4hLRRYy54NzeU539MUVlnUktkuPUkb270BbCgch5K2fngC2ERaYPC35Ea/TXqemfXYCDwJ\nfLwgcz6wJcu8juz5oo1/9xOANVXy2lWeHyQZwDeAV4CthbyfyfrvIA1dNaU8R6ojsAT4VVXdnJfv\n/RuwOev5j8CvtassgQ9nXTbm8/kFmYfn+rEj15eJbf6bzyC9sI2pktmO8vxX4GeFv+2KZtRNu8Ex\nxhjTMkbbnI4xxphRjI2OMcaYlmGjY4wxpmXY6BhjjGkZNjrGGGNaho2O2Weo8iy8QQUP4zXSzpC0\nZaD7rUTSfEnX5vAJkj5cuHehpHNbqMs8SQta9TzTeTRtu2pj2sAvI6JhF/CtJiK6ge58eQLwOvBY\nvnfDANlGjKRx0ed/rJp5pG8vHiz7ucaAezpmHyf3aP4j72fyZLEXUUgzR9La3DvaJGlWjl9SiP8H\nSWNr5H1O0jU53VpJ78/x7817klT2Jpme4/9I0hZJGyX9KMedIOmB3DO7ELgkP/N4SVdJulTSkZLW\nVv2uTTl8THa6+oSkldmXV7WeyyT9jaRVwDWSjpX0WHbY+pikI5T2lrkaWJifv1DSJEk3S1qX0zbq\ngd10Oo1+5erDx95yALvp+5r6vhz3LqArh2cB3Tk8g7wfEPB3JOefkFyE7AccCdwPjM/x15N84VU/\n8zn6vu4+F3ggh+8HlubwZ4B/yeHNwNQcruxFc0Ih31XApQX571zn31XxCPEVkveK8aRe0ZQcvxC4\nuYaey4AH6HNdNBkYl8OnAP+cw+cB1xXy/SWwpKIv6Yv2Se3+W/sYvYeH18y+RK3htfHAdUo7L+4m\nOSWtZjVweXa0em9EbJd0MnAMsC7veLAfaf+YWtxZOH8nh48D/jCHbwe+lcOPAsskLQfuHc6PI21A\n9ingmyTjshA4grTZ30NZz7EkD+C1uCcidufwAcCtuVcXpHKqxe8Dp0u6NF93AdOBp4apuzGA53TM\nvs8lJH9Sv00aTn6zOkFE3CHpceBjwEpJf0JyYHhrRFxWxzNigHC/NBFxYd5m4WPAcLchvhu4R9K9\nSVRslzSX5MvruDryv1EIfx1YFRGfzMN6jwyQRyTns08PQ09jBsRzOmZf5wDgxUj7vpxD6gnsgaTD\nSdtYXwusIG0t/DBwlqRDc5qDVdgzvoqFhfPqHH6M5O0aYDHwn1nO+yLi8Yj4GsnVftFlPMBrwP61\nHhJpm4vdwJUkAwTJee0UScdl+ePzXkxDcQDJ0SSkIbWBnr8SuFi5GyXp6DpkGzMgNjpmX+d6YKmk\nNaShtTdqpFkIbJG0gbRx1m0RsY00Z/LDPGH/ENBvgj4zMfeU/ozUswL4IvDpnPecfA/g25I25+Xa\nPyJ5Qi5yP/DJykKCGs+6m+TteTlApK2HzyItDthImvfpt1iiBt8C/krSo+xpiFcBsysLCUg9ovHA\npqzz1+uQbcyA2Mu0MQ0g6TlgfkS83G5djBkNuKdjjDGmZbinY4wxpmW4p2OMMaZl2OgYY4xpGTY6\nxhhjWoaNjjHGmJZho2OMMaZl/D9iLkiSOLTOhAAAAABJRU5ErkJggg==\n", 197 | "text/plain": [ 198 | "" 199 | ] 200 | }, 201 | "metadata": {}, 202 | "output_type": "display_data" 203 | } 204 | ], 205 | "source": [ 206 | "plt.figure(1)\n", 207 | "plt.plot([0, 1], [0, 1], 'k--')\n", 208 | "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n", 209 | "plt.plot(fpr_rf, tpr_rf, label='RF (area = {:.3f})'.format(auc_rf))\n", 210 | "plt.xlabel('False positive rate')\n", 211 | "plt.ylabel('True positive rate')\n", 212 | "plt.title('ROC curve')\n", 213 | "plt.legend(loc='best')\n", 214 | "plt.show()\n", 215 | "# Zoom in view of the upper left corner.\n", 216 | "plt.figure(2)\n", 217 | "plt.xlim(0, 0.2)\n", 218 | "plt.ylim(0.8, 1)\n", 219 | "plt.plot([0, 1], [0, 1], 'k--')\n", 220 | "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n", 221 | "plt.plot(fpr_rf, tpr_rf, label='RF (area = {:.3f})'.format(auc_rf))\n", 222 | "plt.xlabel('False positive rate')\n", 223 | "plt.ylabel('True positive rate')\n", 224 | "plt.title('ROC curve (zoomed in at top left)')\n", 225 | "plt.legend(loc='best')\n", 226 | "plt.show()" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "metadata": {}, 232 | "source": [ 233 | "## (Optional) Prediction probability density function(PDF)" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": 7, 239 | "metadata": {}, 240 | "outputs": [], 241 | "source": [ 242 | "import numpy as np\n", 243 | "from scipy.interpolate import UnivariateSpline\n", 244 | "from matplotlib import pyplot as plt\n", 245 | "\n", 246 | "def plot_pdf(y_pred, y_test, name=None, smooth=500):\n", 247 | " positives = y_pred[y_test == 1]\n", 248 | " negatives = y_pred[y_test == 0]\n", 249 | " N = positives.shape[0]\n", 250 | " n = N//smooth\n", 251 | " s = positives\n", 252 | " p, x = np.histogram(s, bins=n) # bin it into n = N//10 bins\n", 253 | " x = x[:-1] + (x[1] - x[0])/2 # convert bin edges to centers\n", 254 | " f = UnivariateSpline(x, p, s=n)\n", 255 | " plt.plot(x, f(x))\n", 256 | "\n", 257 | " N = negatives.shape[0]\n", 258 | " n = N//smooth\n", 259 | " s = negatives\n", 260 | " p, x = np.histogram(s, bins=n) # bin it into n = N//10 bins\n", 261 | " x = x[:-1] + (x[1] - x[0])/2 # convert bin edges to centers\n", 262 | " f = UnivariateSpline(x, p, s=n)\n", 263 | " plt.plot(x, f(x))\n", 264 | " plt.xlim([0.0, 1.0])\n", 265 | " plt.xlabel('density')\n", 266 | " plt.ylabel('density')\n", 267 | " plt.title('PDF-{}'.format(name))\n", 268 | " plt.show()" 269 | ] 270 | }, 271 | { 272 | "cell_type": "code", 273 | "execution_count": 8, 274 | "metadata": { 275 | "scrolled": true 276 | }, 277 | "outputs": [ 278 | { 279 | "data": { 280 | "image/png": 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HjDFBY0wDsA2Yn612qyxqb7KyEpH+z3XDX2NdazBJizGGQDDStS5XvGFOEV4zE9WPrNZM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\ns0czqib9IcGOqgyP5orZXVw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BTRFRSbv1A4vS8iJgXTq2kvY/oDF9lGOeJzVSWC1p9YYNG8aTbTOzjjRYruY2lgzsIchI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\nxdyzbhOf/v5D/Ic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291 | "text/plain": [ 292 | "" 293 | ] 294 | }, 295 | "metadata": {}, 296 | "output_type": "display_data" 297 | } 298 | ], 299 | "source": [ 300 | "plot_pdf(y_pred_rf, y_test, 'rf')\n", 301 | "plot_pdf(y_pred_keras, y_test, 'Keras')" 302 | ] 303 | }, 304 | { 305 | "cell_type": "markdown", 306 | "metadata": {}, 307 | "source": [ 308 | "# Multi-class classification" 309 | ] 310 | }, 311 | { 312 | "cell_type": "markdown", 313 | "metadata": {}, 314 | "source": [ 315 | "### Generate some train/test data\n", 316 | "3 classes to classify" 317 | ] 318 | }, 319 | { 320 | "cell_type": "code", 321 | "execution_count": 9, 322 | "metadata": {}, 323 | "outputs": [ 324 | { 325 | "name": "stdout", 326 | "output_type": "stream", 327 | "text": [ 328 | "[[0 0 1]\n", 329 | " [0 1 0]]\n" 330 | ] 331 | } 332 | ], 333 | "source": [ 334 | "from sklearn.datasets import make_classification\n", 335 | "from sklearn.preprocessing import label_binarize\n", 336 | "\n", 337 | "# 3 classes to classify\n", 338 | "n_classes = 3\n", 339 | "\n", 340 | "X, y = make_classification(n_samples=80000, n_features=20, n_informative=3, n_redundant=0, n_classes=n_classes,\n", 341 | " n_clusters_per_class=2)\n", 342 | "\n", 343 | "# Binarize the output\n", 344 | "y = label_binarize(y, classes=[0, 1, 2])\n", 345 | "n_classes = y.shape[1]\n", 346 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)\n", 347 | "print(y[:2])" 348 | ] 349 | }, 350 | { 351 | "cell_type": "markdown", 352 | "metadata": {}, 353 | "source": [ 354 | "### Build and train Keras model" 355 | ] 356 | }, 357 | { 358 | "cell_type": "code", 359 | "execution_count": 10, 360 | "metadata": {}, 361 | "outputs": [ 362 | { 363 | "name": "stdout", 364 | "output_type": "stream", 365 | "text": [ 366 | "Epoch 1/10\n", 367 | "40000/40000 [==============================] - 2s 48us/step - loss: 0.5684 - acc: 0.7848\n", 368 | "Epoch 2/10\n", 369 | "40000/40000 [==============================] - 2s 43us/step - loss: 0.3803 - acc: 0.8718\n", 370 | "Epoch 3/10\n", 371 | "40000/40000 [==============================] - 2s 41us/step - loss: 0.3613 - acc: 0.8778\n", 372 | "Epoch 4/10\n", 373 | "40000/40000 [==============================] - 2s 41us/step - loss: 0.3491 - acc: 0.8816\n", 374 | "Epoch 5/10\n", 375 | "40000/40000 [==============================] - 2s 40us/step - loss: 0.3387 - acc: 0.8849\n", 376 | "Epoch 6/10\n", 377 | "40000/40000 [==============================] - 2s 39us/step - loss: 0.3300 - acc: 0.8874\n", 378 | "Epoch 7/10\n", 379 | "40000/40000 [==============================] - 2s 39us/step - loss: 0.3232 - acc: 0.8896\n", 380 | "Epoch 8/10\n", 381 | "40000/40000 [==============================] - 2s 38us/step - loss: 0.3166 - acc: 0.8917\n", 382 | "Epoch 9/10\n", 383 | "40000/40000 [==============================] - 2s 38us/step - loss: 0.3115 - acc: 0.8944\n", 384 | "Epoch 10/10\n", 385 | "40000/40000 [==============================] - 2s 39us/step - loss: 0.3078 - acc: 0.8953\n" 386 | ] 387 | }, 388 | { 389 | "data": { 390 | "text/plain": [ 391 | "" 392 | ] 393 | }, 394 | "execution_count": 10, 395 | "metadata": {}, 396 | "output_type": "execute_result" 397 | } 398 | ], 399 | "source": [ 400 | "from keras.models import Sequential\n", 401 | "from keras.layers import Dense\n", 402 | "\n", 403 | "def build_model():\n", 404 | " model = Sequential()\n", 405 | " model.add(Dense(20, input_dim=20, activation='relu'))\n", 406 | " model.add(Dense(40, activation='relu'))\n", 407 | " model.add(Dense(3, activation='softmax'))\n", 408 | " # Compile model\n", 409 | " model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", 410 | " return model\n", 411 | "\n", 412 | "keras_model2 = build_model()\n", 413 | "keras_model2.fit(X_train, y_train, epochs=10, batch_size=100, verbose=1)" 414 | ] 415 | }, 416 | { 417 | "cell_type": "markdown", 418 | "metadata": {}, 419 | "source": [ 420 | "### Make prediction for test inputs" 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": 11, 426 | "metadata": {}, 427 | "outputs": [], 428 | "source": [ 429 | "y_score = keras_model2.predict(X_test)" 430 | ] 431 | }, 432 | { 433 | "cell_type": "markdown", 434 | "metadata": {}, 435 | "source": [ 436 | "### Plot ROC for each of the 3 classes\n", 437 | "\n", 438 | "Use micro and marco averaging to evaluate the overall performance across all classes.\n", 439 | "$$\n", 440 | "precision=PRE=\\frac{TP}\n", 441 | "{TP+FP}\\\\\n", 442 | "$$\n", 443 | " In “micro averaging”, we’d calculate the performance, e.g., precision, from the individual true positives, true negatives, false positives, and false negatives of the the k-class model:\n", 444 | "$$\n", 445 | "PRE_{micro}=\\frac{TP_{1}+\\dots+TP_{k}}\n", 446 | "{TP_{1}+\\dots+TP_{k}+FP_{1}+\\dots+FP_{k}}\\\\\n", 447 | "$$\n", 448 | "And in macro-averaging, we average the performances of each individual class:\n", 449 | "$$\n", 450 | "PRE_{marco}=\\frac{PRE_{1}+\\dots+PRE_{k}}\n", 451 | "{k}\\\\\n", 452 | "$$" 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": 12, 458 | "metadata": {}, 459 | "outputs": [ 460 | { 461 | "data": { 462 | "image/png": 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rN4A+wGTgciBtbyJUrb2GHVHIepZpO4u4vXS3ucMNk49i3DXHsjw7yOZdYYoD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Murzic+inn7bi8XiJbNMMbhnqk/aCC/rz5JM/EhcXxbBhHenUKckn/kC+l137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\nXCDCcQv1J6CSPrKlIbMHGOv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Zu4vI0Rhv8veLyBLgHVV9J+y1s1TL085/P6C+K46mT19D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473 | "text/plain": [ 474 | "" 475 | ] 476 | }, 477 | "metadata": {}, 478 | "output_type": "display_data" 479 | } 480 | ], 481 | "source": [ 482 | "import numpy as np\n", 483 | "from scipy import interp\n", 484 | "import matplotlib.pyplot as plt\n", 485 | "from itertools import cycle\n", 486 | "from sklearn.metrics import roc_curve, auc\n", 487 | "\n", 488 | "# Plot linewidth.\n", 489 | "lw = 2\n", 490 | "\n", 491 | "# Compute ROC curve and ROC area for each class\n", 492 | "fpr = dict()\n", 493 | "tpr = dict()\n", 494 | "roc_auc = dict()\n", 495 | "for i in range(n_classes):\n", 496 | " fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n", 497 | " roc_auc[i] = auc(fpr[i], tpr[i])\n", 498 | "\n", 499 | "# Compute micro-average ROC curve and ROC area\n", 500 | "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\n", 501 | "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n", 502 | "\n", 503 | "# Compute macro-average ROC curve and ROC area\n", 504 | "\n", 505 | "# First aggregate all false positive rates\n", 506 | "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n", 507 | "\n", 508 | "# Then interpolate all ROC curves at this points\n", 509 | "mean_tpr = np.zeros_like(all_fpr)\n", 510 | "for i in range(n_classes):\n", 511 | " mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n", 512 | "\n", 513 | "# Finally average it and compute AUC\n", 514 | "mean_tpr /= n_classes\n", 515 | "\n", 516 | "fpr[\"macro\"] = all_fpr\n", 517 | "tpr[\"macro\"] = mean_tpr\n", 518 | "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n", 519 | "\n", 520 | "# Plot all ROC curves\n", 521 | "plt.figure(1)\n", 522 | "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n", 523 | " label='micro-average ROC curve (area = {0:0.2f})'\n", 524 | " ''.format(roc_auc[\"micro\"]),\n", 525 | " color='deeppink', linestyle=':', linewidth=4)\n", 526 | "\n", 527 | "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n", 528 | " label='macro-average ROC curve (area = {0:0.2f})'\n", 529 | " ''.format(roc_auc[\"macro\"]),\n", 530 | " color='navy', linestyle=':', linewidth=4)\n", 531 | "\n", 532 | "colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n", 533 | "for i, color in zip(range(n_classes), colors):\n", 534 | " plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n", 535 | " label='ROC curve of class {0} (area = {1:0.2f})'\n", 536 | " ''.format(i, roc_auc[i]))\n", 537 | "\n", 538 | "plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n", 539 | "plt.xlim([0.0, 1.0])\n", 540 | "plt.ylim([0.0, 1.05])\n", 541 | "plt.xlabel('False Positive Rate')\n", 542 | "plt.ylabel('True Positive Rate')\n", 543 | "plt.title('Some extension of Receiver operating characteristic to multi-class')\n", 544 | "plt.legend(loc=\"lower right\")\n", 545 | "plt.show()\n", 546 | "\n", 547 | "\n", 548 | "# Zoom in view of the upper left corner.\n", 549 | "plt.figure(2)\n", 550 | "plt.xlim(0, 0.2)\n", 551 | "plt.ylim(0.8, 1)\n", 552 | "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n", 553 | " label='micro-average ROC curve (area = {0:0.2f})'\n", 554 | " ''.format(roc_auc[\"micro\"]),\n", 555 | " color='deeppink', linestyle=':', linewidth=4)\n", 556 | "\n", 557 | "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n", 558 | " label='macro-average ROC curve (area = {0:0.2f})'\n", 559 | " ''.format(roc_auc[\"macro\"]),\n", 560 | " color='navy', linestyle=':', linewidth=4)\n", 561 | "\n", 562 | "colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n", 563 | "for i, color in zip(range(n_classes), colors):\n", 564 | " plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n", 565 | " label='ROC curve of class {0} (area = {1:0.2f})'\n", 566 | " ''.format(i, roc_auc[i]))\n", 567 | "\n", 568 | "plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n", 569 | "plt.xlabel('False Positive Rate')\n", 570 | "plt.ylabel('True Positive Rate')\n", 571 | "plt.title('Some extension of Receiver operating characteristic to multi-class')\n", 572 | "plt.legend(loc=\"lower right\")\n", 573 | "plt.show()" 574 | ] 575 | }, 576 | { 577 | "cell_type": "code", 578 | "execution_count": null, 579 | "metadata": {}, 580 | "outputs": [], 581 | "source": [] 582 | } 583 | ], 584 | "metadata": { 585 | "kernelspec": { 586 | "display_name": "Python 3", 587 | "language": "python", 588 | "name": "python3" 589 | }, 590 | "language_info": { 591 | "codemirror_mode": { 592 | "name": "ipython", 593 | "version": 3 594 | }, 595 | "file_extension": ".py", 596 | "mimetype": "text/x-python", 597 | "name": "python", 598 | "nbconvert_exporter": "python", 599 | "pygments_lexer": "ipython3", 600 | "version": "3.5.2" 601 | } 602 | }, 603 | "nbformat": 4, 604 | "nbformat_minor": 2 605 | } 606 | --------------------------------------------------------------------------------