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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAABgCAYAAAAehMmeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAABtpJREFUeJzt3E1IVHscxvHnpNGYExYOXbMoNahFEJgFFkZRLkqiFiKRiwpaZJa0CDeBJRFESEUR0SKkRRGVixYxSBTRiwbuXVnpptBqotcp3zp34U3sqqMX5szfO7/vB87COQrPPHN40KOj5/u+AADpb5brAACA1GDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcCITNcBJElZWb36+fMv1zECFwr16cePvMlOZ2Wp9+dPpXUPoZD6fvyQ6Q6kxD3QAYLizYj/peN5vmZCjqB5nuT7XoLTM+LlCNJIBTLdgZS4BzpAULilAwBGMPgAYASDDwBGMPgAYASDDwBGMPgAYASDDwBGMPgAYASDDwBGMPgAYASDDwBGMPgAYASDDwBGMPgAYASDDwBGMPgAYASDDwBGMPgAYASDDwBGMPgAYASDDwBGMPgAYETaDn5jY6M8zxs98vPzVVlZqVevXkmSXr58qYMHD2r16tXKyMjQ5s2b3QYOwFQd3L17Vzt37tTixYsVDodVUlKiW7duOU6dfFP10NLSog0bNig3N1ehUEgrV67U6dOnNTAw4Dh58kzVwVhv3rxROByW53n69u2bg7QISqbrAEHKyclRa2urJOn169dqaGjQ1q1b1dnZqc7OTkWjUZWWlmpwcNBx0uAk6uD8+fMqLCzUhQsXFIlEFI1GVV1drQ8fPqiurs5x8uRK1EMsFtOWLVtUX1+v+fPnq6OjQ42Njert7dXly5cdJ0+eRB1kZ2ePfl59fb3C4bC+f//uKiqC4vu++0Pyk+3kyZN+bm7uH489e/bMl+TfuXPHHx4eHn28srLS37RpU9IzjDPyPCftIdk1TNXB+/fvx33Nnj17/IKCguQGGeOf55iyDnx/6h4mcvz4cT8nJ8f/9etX8gP5iXtw2cGTJ0/8BQsW+E1NTb4k/+vXr8kP84+prgWO5B9pe0tnIiUlJZKknp4ezZpl6qmPGttBJBIZd764uFhv375NdayUG9vDRHJzc9Pqls5E/t3B8PCw6urqdOLEiQmvDfz/mVq93xd2Xl6e2yAOTdXBixcvtGLFihQmcmOiHoaHhxWPx/X8+XNdunRJhw4dkud5jhIG798dXL16Vf39/Tp8+LDDVAhSWt/Dl6ShoSFJI/csa2trNW/ePJWXlztOlVrT7eDRo0e6d++empubUx0xJabqITs7W/39/ZKkvXv3qqmpyUnOIE3WQSwWU0NDg27cuKHZs2c7TomgpPXgx2KxPy7epUuX6vbt21q0aJHDVKk13Q56enpUXV2tXbt2af/+/SlOGbzp9NDe3q54PK6Ojg6dOnVKR44c0ZUrV1zEDUSiDmpqalRaWqqKigqHCRG0tB78nJwcPXz4UJ7nKS8vT/n5+Wn9I/pEptPBx48ftX37di1btkw3b950lDRY0+lhzZo1kqSysjJFIhHt27dPx44d0/Lly11ETrrJOujs7FRzc7OePn2qT58+SZLi8bgk6fPnz8rIyFBWVpbL6EiStB78zMxMrV271nUMp6bqIB6Pa8eOHRoYGND9+/c1d+7cFKZLnf96Lfwe/+7u7rQZ/Mk66Orq0uDgoNavXz/u3JIlS3TgwAFdu3YtFRERsLQefCQ2NDSkqqoqdXV1qb29XQsXLnQdacZoa2uTJBUWFjpOEryysjI9fvz4j8daW1t19uxZRaNRFRUVOUqGZDM7+PF4XNFoVNLIOwu/fPmilpYWSVJFRUXafqc7Vm1traLRqC5evKhYLKZYLDZ6rri4WHPmzHGYLnW2bdum8vJyrVq1ShkZGWpra9O5c+e0e/futPnuPpFIJDLunea//4Jn48aNCofDqQ+FQJgd/Hfv3qmqquqPx35/3N3drYKCAgepUuvBgweSpKNHj447Z6UDSVq3bp2uX7+unp4eZWZmqqioSGfOnFFNTY3raEBSeb7vu84geZ6vmZAjaJ4n+f6kvzX2PM2IlyNIIxXIdAdS4h7oAEEx9cYrALCMwQcAIxh8ADCCwQcAIxh8ADCCwQcAIxh8ADCCwQcAIxh8ADCCwQcAIxh8ADCCwQcAIxh8ADCCwQcAIxh8ADCCwQcAIxh8ADCCwQcAIxh8ADCCwQcAIxh8ADCCwQcAIxh8ADAi03UASVIo1CfP+8t1jMCFQn2JT6vP85TWPYRCMt+BlLgHOkBQPN/3XWcAAKQAt3QAwAgGHwCMYPABwAgGHwCMYPABwAgGHwCMYPABwAgGHwCMYPABwAgGHwCMYPABwAgGHwCMYPABwAgGHwCMYPABwAgGHwCMYPABwAgGHwCMYPABwAgGHwCMYPABwAgGHwCMYPABwIi/ATv2A0/KczaXAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x81 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x81 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x81 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAABgCAYAAAAehMmeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAABuFJREFUeJzt3E1IVH0fxvHrpNGYExYO3WZRalCLIDALLIyiXJRELUQiFxW0yCxpEW4CSyKIkIoiokVIiyIqFy1ikCiiFw3cu7LSTaHVRK9TvnWehU9ipTPeMGf+3fP7fmAWnqNwzcXhYjzO6Pm+LwBA5pvhOgAAID0YfAAwgsEHACMYfAAwgsEHACMYfAAwgsEHACMYfAAwgsEHACMYfAAwItt1AEnKyVH/9+/6x3WOoIVCGvj2TQVTnbfQAx2MSdhDTk6/vn/P+A4UCg3o27cprwWknvc3/C8dz9PfECNwnif5vrwE5zO+BzoYk7AHz/Nlp4QprwWkHrd0AMAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjMjYwW9ubpbneeOPwsJCVVdX68WLF5Kk58+fa//+/Vq5cqWysrK0ceNGt4EDkKyD27dva/v27Vq4cKHC4bDKysp048YNx6lTL1kPbW1tWrdunfLz8xUKhbR8+XKdPHlSQ0NDjpOnTrIOJnr16pXC4bA8z9OXL18cpEVQsl0HCFJeXp7a29slSS9fvlRTU5M2b96s7u5udXd3KxqNqry8XMPDw46TBidRB2fPnlVxcbHOnTunSCSiaDSq2tpavXv3Tg0NDY6Tp1aiHmKxmDZt2qTGxkbNnTtXXV1dam5uVn9/vy5evOg4eeok6iA3N3f8+xobGxUOh/X161dXUREU3/edPyQ/5Y4fP+7n5+f/cuzJkye+JP/WrVv+6Ojo+PHq6mp/w4YNqQ/xm/8/z7T1kKyDt2/f/vEzu3bt8ouKilIbZIJ0d+D7yXuYzNGjR/28vDz/x48fqQ/kJ+khgBKm28GjR4/8efPm+S0tLb4k//PnzynPMm7seTrfH0uPjL2lM5mysjJJUl9fn2bMMPXUx03sIBKJ/HG+tLRUr1+/TnestJvYw2Ty8/Mz6pbOZH7vYHR0VA0NDTp27Nik1wb++0yt3s8Lu6CgwG0Qh5J18OzZMy1btiyNidyYrIfR0VHF43E9ffpUFy5c0IEDB+R5nqOEwfu9g8uXL2twcFAHDx50mApByuh7+JI0MjIiaeyeZX19vebMmaPKykrHqdJruh08ePBAd+7cUWtra7ojpkWyHnJzczU4OChJ2r17t1paWpzkDNJUHcRiMTU1NenatWuaOXOm45QISkYPfiwW++XiXbx4sW7evKkFCxY4TJVe0+2gr69PtbW12rFjh/bu3ZvmlMGbTg+dnZ2Kx+Pq6urSiRMndOjQIV26dMlF3EAk6qCurk7l5eWqqqpymBBBy+jBz8vL0/379+V5ngoKClRYWJjRv6JPZjodvH//Xlu3btWSJUt0/fp1R0mDNZ0eVq1aJUmqqKhQJBLRnj17dOTIES1dutRF5JSbqoPu7m61trbq8ePH+vDhgyQpHo9Lkj5+/KisrCzl5OS4jI4UyejBz87O1urVq13HcCpZB/F4XNu2bdPQ0JDu3r2r2bNnpzFd+vzba+Hn+Pf29mbM4E/VQU9Pj4aHh7V27do/zi1atEj79u3TlStX0hERAcvowUdiIyMjqqmpUU9Pjzo7OzV//nzXkf4aHR0dkqTi4mLHSYJXUVGhhw8f/nKsvb1dp0+fVjQaVUlJiaNkSDWzgx+PxxWNRiWNfbLw06dPamtrkyRVVVVl7Cvdierr6xWNRnX+/HnFYjHFYrHxc6WlpZo1a5bDdOmzZcsWVVZWasWKFcrKylJHR4fOnDmjnTt3Zsyr+0QikcgfnzT/+Q6e9evXKxwOpz8UAmF28N+8eaOamppfjv38ure3V0VFRQ5Spde9e/ckSYcPH/7jnJUOJGnNmjW6evWq+vr6lJ2drZKSEp06dUp1dXWuowEp5fm+7zqDPE9/Q4zAeZ7k+5ryr8YWeqCDMQl78Dxfdkqw9S4Kx0x98AoALGPwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjGDwAcAIBh8AjMh2HUCSQiENeJ7+cZ0jaKGQBpKdz/Qe6GBMwh5CoQF5XsZ3oFAo4bWA1PN833edAQCQBtzSAQAjGHwAMILBBwAjGHwAMILBBwAjGHwAMILBBwAjGHwAMILBBwAjGHwAMILBBwAjGHwAMILBBwAjGHwAMILBBwAjGHwAMILBBwAjGHwAMILBBwAjGHwAMILBBwAjGHwAMILBBwAj/gc79gNPfO/4cQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x81 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.patches import Rectangle\n",
"from matplotlib.text import Text\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"%matplotlib inline\n",
"\n",
"scale = .75\n",
"\n",
"for run in (0, 1, 2, 3):\n",
" fig = plt.figure(figsize=(scale * 8, scale * 1.5))\n",
" ax = plt.subplot(1, 1, 1)\n",
" ax.set_axis_off()\n",
"\n",
" for p in (0, 1, 2, 3):\n",
" r = Rectangle((x0+ .01, 0.01), .23 * scale, .98, figure=fig, facecolor=\"w\", edgecolor=color, linewidth=1)\n",
" plt.text(.09 * scale + x0, .55 * scale, \"P\" + str(p + 1), fontsize=20 * scale)\n",
" plt.savefig(\"cross_val_%d.svg\" % run)"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x129.6 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(scale * 8, scale * 2.4))\n",
"ax = plt.subplot(1, 1, 1)\n",
"ax.set_axis_off()\n",
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"ax.set_ylim([0, 1.5])\n",
"\n",
"for p in (0, 1, 2, 3):\n",
" x0 = .24 * p * scale\n",
" color = \"b\"\n",
" r = Rectangle((x0 + 0.02, 0.1), .22 * scale, .8 , figure=fig, facecolor=\"w\", edgecolor=color, linewidth=1)\n",
" ax.add_patch(r)\n",
" plt.text(x0 + scale * 0.1, .54 * scale, \"P\" + str(p + 1), fontsize=20 * scale)\n",
"\n",
"\n",
"\n",
"\n",
"r = Rectangle((0.005, 0.02), 0.738, .98, figure=fig, alpha=0.5, facecolor=\"w\", edgecolor=\"k\", linewidth=1)\n",
"ax.add_patch(r);\n",
"\n",
"\n",
"r = Rectangle((.8, 0.1), .22 * scale, .8, figure=fig, facecolor=\"w\", edgecolor=\"g\", linewidth=1)\n",
"ax.add_patch(r);\n",
"\n",
"plt.text(.87, .54 * scale, \"V\" , fontsize=20 * scale)\n",
"plt.text(0.25, 1.2, \"crosseval\", fontsize=20 * scale)\n",
"plt.text(.78, 1.2, \"validation\", fontsize=20 * scale)\n",
"\n",
"plt.savefig(\"cross_eval_and_test.svg\", bbox=\"tight\")\n",
"\n",
"plt.savefig(\"cross_eval_and_test.png\", bbox=\"tight\")\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rectangle(xy=(0.5725, 0.01), width=0.18, height=0.98, angle=0)\n"
]
}
],
"source": [
"print(r)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x129.6 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x129.6 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def create_plot(x0, name):\n",
" fig = plt.figure(figsize=(scale * 8, scale * 2.4))\n",
"\n",
" ax = plt.subplot(1, 1, 1)\n",
" ax.set_title(\"higher recall, lower precision\")\n",
" ax.set_axis_off()\n",
" ax.set_ylim([0, 0.85])\n",
" ax.set_xlim([0, 2])\n",
"\n",
" ax.vlines(x0, 0.2, 0.8)\n",
" ax.arrow(1, 0.5, -0.8, 0, head_width=0.08, head_length=0.05, fc='k', ec='k')\n",
" ax.arrow(1, 0.5, 0.8, 0, head_width=0.08, head_length=0.05, fc='k', ec='k')\n",
" ax.text(0.25, 0.2, \"irrelevant results\")\n",
" ax.text(1.3, 0.2, \"relevant results\");\n",
"\n",
" plt.savefig(name)\n",
" \n",
"create_plot(0.85, \"precision_low_recall_high.svg\")\n",
"create_plot(1.15, \"precision_high_recall_low.svg\")"
]
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}