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sinhrks/add_featurenames.ipynb

Last active February 22, 2018 06:13
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add_featurenames.ipynb
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add_featuretypes.ipynb
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pandas+cv.ipynb
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visualize_xgboost.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'0.4'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import xgboost as xgb\n",
"from sklearn import datasets\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"\n",
"xgb.__version__"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris = datasets.load_iris()\n",
"dm = xgb.DMatrix(iris.data, label=iris.target)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(1) \n",
"\n",
"params={'objective': 'multi:softprob',\n",
" 'eval_metric': 'mlogloss',\n",
" 'eta': 0.3,\n",
" 'num_class': 3}\n",
"\n",
"bst = xgb.train(params, dm, num_boost_round=18)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10300d490>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AEBFJigFARCQpBgARkaQYAEREkmIAEBFJigFARCQpBgARkaQYAEREkmIAEBFJigFARCQp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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10637cf90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xgb.plot_importance(bst)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x103018750>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x106411d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# overwrite colors and labels\n",
"xgb.plot_importance(bst, color='red', title='title', xlabel='x', ylabel='y')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1066c6050>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10641b610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# overwrite colors and disable labels\n",
"xgb.plot_importance(bst, color=['r', 'r', 'b', 'b'], title=None, xlabel=None, ylabel=None)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
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"source": [
"xgb.to_graphviz(bst, num_trees=1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
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"metadata": {},
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],
"source": [
"# change edge colors\n",
"xgb.to_graphviz(bst, num_trees=1, yes_color='#FF0000', no_color='#0000FF')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
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U4fucnZ2RkJCArKysUm/Qeu/ePdStWxd9+/YF/40phchIYN8+fWch4B6CFoSE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\nMSb4f+Cy/US6kvpzAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10673e850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xgb.plot_tree(bst, num_trees=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1076a36d0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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t0lKG73JyckJCQgKysrJKvUDr3bt3Ua9ePfTr1w/8N6bkIiOBffv0ncW/uIeg\nBSEhIZBKpWjTpo0o7bVq1QqBgYGitFUYIkLfvn3h7e0tSnv79+/H8OHDUbNmTVHaK+9u3lTOoaDj\nzmChDCCF8uXKlSuoXbu2aMUAUD68NHHiRNHae5NCocD69etFKwYAMGXKFLx48QLx8fGitVmede0K\ntGypfB0Xp99cuIcgovDwcNStWxfVqlXTSvvt27fHQ5EXAhg7diyOHDkiapv5/P398dFHH2nt+1Gs\nLVuA+fOVrw38xzw7G6heXXmx8Y3rzTrHPQSREBH8/Py0+sP/8OFDjB07VrT2OnbsqLViAADt2rXD\n2rVrtdZ+sfKHVBrALdvi1KwJ5OYqh5/oE/cQRNKyZUs8e/ZM63Fev36NKlWqoGrVqhq1ExAQABsb\nG43XOiwJR0dHnDx5UutxCmVurlyMceFC/cQvhTlzgO3bgdRUIG8iJ53jgiCCzMxMVKlSpdRX5tXV\npEkTREREaNTGuHHjcPjwYXESKkZWVhbMzc1VHuPj44PMzEzRY1vcuoWkzp210kvo1asXqlSpUui+\nf/75R6024+JMcPduY9jbR2iQWdFq1Kiheso73U6/UD4NHjxY5zEzMjLUPtff31/ETEpm2rRpKvdb\nWFjoKBPxXLx4sch9+vgel8RHH32kcj9fQxCBPuapnaTBys4L9dB9trW11XlMVnpcEDSUlJQEOzs7\nncfVJOayZctEzKRkZs2apfOYrPS4IGioX79+eon75ZdfqjUiMCMjA71799ZCRsXbvHmzXuKykuOC\noKFQHa+clK9evXqQy+WlPi82NlYL2ZTMunXr9BablQwXBA2NGjVK5f7ff/8dMTEx+OKtqdMB5cCd\n8PBw4es1a9YI06bb2Nj8++RjEdLS0kqd79mzZ1Xud3V1Rc+ePTFr1qx3bhUqFIoCdwvkcjlsbGyQ\nnp4ONzc3IfeilPRBrQMHDhS5b8eOHYgrZDhfq1at8PHHH0MikQAAZDIZdu7cCQDC9/HChQsFPmrl\nL6zSokULnDp16p02PTw8cPToUQDK6y6jR48uUf5vW7JkSZH7IiMjIZPJ3tnevXt3TJgwQfi6R48e\neP/99wFAGOuyceNGNGzYEIDy52zIkCEAIIySHTNmDCpWrFiqXLV/E7qca6LigfawsDAkJyejQYMG\nBYYer1mzBr1790b37t0L3D709fXFsGHDIJFIkJqaWmyxefHiBSwsLEqVb3E9hJ9//hm+vr6oWLEi\ncnNzAQCOEberAAARh0lEQVSJiYmYOXMmjh49WqAgnDp1CpmZmahevTpcXV3RrVs3lW03atSoRDkO\nGDAAgLIw3L17F1ZWVsjMzMT06dORnJyMpKQkJCQkAABat24NQDm8283NTbgNWKtWLYwcORLTpk1D\nVFQUwsLCMGDAANzNm9TQ1dUV5ubmiIyMREJCAoYPH45ff/0Vt2/fxsGDBwEof5H/97//wc3NDSNH\njkR6ejqIqNS3l/N/sS9fvozGjRtj27ZtyMzMxMyZMxEZGYnmzZsLx+a/n/Pnz+P58+fCdl9fX3z2\n2WcAIPQMFy1aJBTH7du3Y8GCBQAgFJijR4/C1dW1VLnybUcNzZgxQ+X+M2fOCK9dXFzI1taW0tLS\niEi5mlJYWFiB47///nvh9YQJE1S2/erVq9KmSxs3blS5v3v37sJrKysrWrx4MV29elXYVrt27QLH\nKxQKevr0KRER7du3T2XbDRs2LHLfm7cdX716RQqFgsLDw4mI6NatW0Le7u7uFBERQeHh4RQeHk5T\np06lNWvWEBHR6NGjhTYWLFhAEomEbty4QUREFStWJCIimUxGz549IxcXF7KyshKOX7p0Kbm6uhIR\n0e3bt2nEiBFkY2NDRER3796ly5cv06JFiwrkXNLbjo8ePaKTJ08KX7u5uVG7du1IJpNRQkKC8F7C\nw8NpxIgRdOPGDUpLSyM/Pz/hHIlEQtnZ2UREBVblSkhIICKimzdvUosWLYiIhH/lcjlJpdICeRV3\n25ELgoZU3T8PCgqiL774gv7++2/65ZdfhO3p6em0cuVKcnd3p02bNhER0fr166lnz5706NEjcnd3\np3Xr1r3zn/k2mUxW6nwDAwNV7q9Vqxbl5uaSs7MzJSUlCds3bdpEUqlU+MXq1asX9erVS3hfbdu2\nLVA4CqPqe/Xmvu3bt9OdO3eoTZs2tHnzZurduzft2bOH7t27R5MnTy6yjfzl6r7++mvy8vKi1atX\nE5Hyl/3mzZs0duxY2rVrl3B8mzZtaMeOHbRx40ZSKBT09ddfk5OTk7Df39+ffvvtNyJSLgeXmZlZ\nIF5JC8LEiRMpKiqKGjZsSNHR0dSoUSNydHQkDw8P2rNnT6Hnz5w5U/je+vn5Ua1atYSCamZmRhKJ\nhFq2bEnHjx8nIiInJyeaP38+ERFVr16d5HI51a5d+50izAVBy/Krsa4lJCQIvwClkd870Yeffvqp\nyH08MEk3eGCSll27dk0vcQ8ePKjWUOnq1avj5s2bWsioePoY/8BKhwuChqysrPDkyROdx7169ara\n5/7000/iJVJCe/fu1XlMVnpcEESgy2nO8v39999qn6vqNpi2nD9/XucxWenx044iiI2NRf369XUW\nr127dvD399eojblz52Lr1q0iZaRa/m0wVffEd+3aJSw5X1aMHTu2yEllt2zZouNsSqZWrVqFjonJ\nxwVBJA0bNkR0dLTW42RlZSEnJ0fj+Qr9/f1ha2urk1mdbW1t8fjxY63HYZrjjwwiefHihajzEhZl\n9OjRokxe2q5dO2GgizatX7+ei0EZwgVBJGZDh8Lc3Fwrk3zk69OnD86cOSNaezdu3NDq9Y/o6GhM\nnjxda+0z8XFBEMOIEYCXF7p06YJz584VWJVJLFOnTsWlS5dEb3ft2rXYtWuX6O3GxMQgNTUVISEW\n0OPzVKy0dDAWovyKiiLavv2dzW5ubhQaGipamCFDhojWVlHmzJkjWlvXr1+noKAg4WuFgui//xWt\neaZFXBDUdfw4UVxckbvT0tLIwcFBoxA5OTkFhtJqW+vWrTVuo3379pSTk/POdoWC6MMPNW6eaRkX\nBHVMm0Ykl5fo0E2bNtGOHTtK1XxcXBz16dOH0tPT1clOI5GRkeTg4FDqORtXrFhR4AGeoowYoW5m\nTBe4IJRWgwZqnZaQkEA1atSgLVu2FLo/Li6OOnbsSAsXLqTc3FxNMhRFTk4OjRo1igYPHvzOQz35\nli5dSo0bNy518bh0iSjvIT1mYHgcQmmMGQPkTZghhsuXJejWrQIqV64sWpvaJJFIQLGxqJo3sYgm\nXr1SfivnzhUhMSYavstQEjk5wI4dohYDAKhSpUqZKQYARFkgJp+1tbIYfPCBKM0xkXBBKE5EhHJ5\n3m+/1Xcm5dLLl4CLC5CUpO9MGMAFQbXgYOW/vXrpNY3ybtkywMKCPz4YAi4IRXFzU/ZrVcyZyMS1\ndSvQubO+szBuXBAKc+oUMGSI/lbcNGK3bwMbNwJaHAHOVOCC8La//gLs7YEydLGvvFm0CAgJAaKi\n9J2J8eGC8KZDh4CpUwEdLJHOVGvXTnmh8Y2ZyJkOcEHId+EC4OTExcCA2NoClSop70Qw3eCCAABP\nnij/JL33ntpNFDVx6YULF9RuszzZsGHDO9syMjKKfTL0gw+AtDTgxQttZcbexAUhLQ2oXh2wsgIA\n7NmzB4ByYo9Zs2bB1tYWDx8+fGddwurVq2PKlCmYMGECFixYIOzPnw9BIpEgJSUF58+fR+XKlYXV\niNasWfPG6ke5GDRoEBo0aAAAuHXrFnbv3q3td6yRwZ9/DgB49OgRdu7ciYULF2L16tWYPr3gvAcd\nOnRA/fr1MXHiROTm5uLnn3/Gy5cv8ccffyA+Ph7BwcHw8fFBRkYGmjVrhg8//BCAckao9u3bF2ir\nTRvlf1P+hcaUlBRMnDgRTZo0QXR0NMLDw/GCK4Y49D12Wu92735n08KFC0mhUAgr+aSnpxdY0YiI\nqF27dsLrvn37kq+vLxERffrppySXy2n48OGUmZlJ3333HdWuXZtSUlKIiGjs2LH0xRdfEBHRzZtE\n9vb29McffwirHlWrVk389yim2Fg6ePAgEf27MEpQUBCNGzeuwGGDBw8md3d3IiJ68uQJ2dnZkVQq\npU6dOpFMJiMbGxvKzMyk169fU/Pmzenly5dEpFzp6v79+4WGdnMjylu8iE6ePEmmpqbUo0cPIiLq\n3Lmz6G/VGBl3D2HSJOVFxLekp6fDxMQEISEhmDx5MjZs2AB7e3sA/64tkJaWhuvXr2Pv3r0IDQ3F\nX3/9haioKCxevBgVKlTA8OHDkZubC39/f1SvXh3//PMPnjx5Am9vb3h6euZFUuD58+fw8PDAuHHj\nMGPGDL1M6V5az549A6BcYDUwMBBr1qxB27ZtcfPmTSgUCgBAQkICDhw4gKdPn2Lr1q2Ij4/Hxo0b\nMWbMGISEhGDx4sW4fPkyvLy8IJPJ8PPPPyM6OhpffPFFkcvGOzoCv/4KSCRZuHbtGqysrHD58mUc\nPHhQJ9PXGQV9VyS9eeMv/JtWrVqlcnUjdVZLevPccePG0YEDB4hI2UMoazo3by6sMfg2TZ/SfPDg\nAY0ZM4YePHig8rjhwzUKw1Qwzqcdp00D8pYK16dbt4AuXfSdRSm9eqUcwaln3bsD16/rO4vyx/g+\nMgQGKsfIsjLt+nXgrWuPTATGUxASEwGJBDA1BapU0Xc28PZW/lB7eOg7kxJKT1cme/484Oen72wA\nAA8fKoc5A0AZW+PFYBnHR4asLGURGDYMOH1a39kAAJYvB/KXWCwz/wP5i8t6ewO9e+s3lzyZmcD7\n7ysfO4mI0Hc2ZZ9xFITvv1c+dG9iApw9C+TdS9c3ExOgceMy9INsZgbI5QZVwfJTAgwqrTLLOD4y\nuLgoPyrk5BhMMcinhaUWtMfNDRBh1Sgx5eYC332nfM2DQjVnMD2E8ePHQ1upfHL+PO4PHFjq8w4f\nPlzo9lWrViEoKEjTtBAW9gGaNhVnoP6hQ4dgkt+lf8u4ceNEiQEAVTMzkSnSNGrjx48Xxne8rbQ5\ny+UmOHduFRwcfhAjtUIV9fNQrujthudbSjtVubYdPXq0yH2Ojo46zKR4UqmUZDKZvtMotWXLlhW5\n79KlSzrMpHgrV67Udwo6YRwfGRhjJcIFgTEm4ILAGBNwQWCMCQyyIATnT39eiIyMDMhkMuHrxYsX\nIzk5GVlZWcK2ESNGAADu3buHJk2awMfHB3K5HKdOnUJ4eDjc3d3x6tUr+Pr6YtCgQQCAli1bqpVr\nZmZmkU8oLlq0CI8fPxa+vnbtGjIzM5GYmAgAkMlksLS0BADs378fHTp0AABERETggw8+QGpqKtzd\n3SGXy3H27FlhroQvv/xSrVwBoE+fPoVuv3HjBqKjo9/ZTkRISkrC8uXLhW3f5d3n++OPP/DLL78g\nMzMTs2bNwpo1awAAx48fR1RUFLZs2YLr169DLpfj1q1baud89+7dQrcTUYH/dwAYNGgQ/P39Ic8b\nnBAfH48lS5ZALpfDz88PPXr0wB9//AGpVIrnz5/D09MT7u7u8Pb2xrVr1zB16lTI5XJh/gpjY5Dz\nhVlYWAAArly5gp07d0KhUKBVq1ZwdnZGdnY2PDw8hIlIbt++jdq1a2P79u2YPXs2Hj58iJkzZwIA\nPv30U3z00Ufo0qULHBwccO7cOZw9exZfffUVXr9+DWtra5jlTZlGat7yrFq1KkxMTLBkyRJcvXoV\ny5cvh6mpKfr164f4+Hjh8WYAWL16NWQyGWxtbREaGoqKFSsKk6VMmTJFeE99+/ZFYGAggoODsWDB\nAtjb22Po0KGwt7eHs7Oz2sULAGrUqIHo6GjExcWhYcOG+PPPP5GcnIxatWrhq6++wo8//ggAaN68\nOcaMGYM1a9bghx9+wJkzZ4R9zfKWcpsxYwakUilMTU2RnJyMtm3bIjMzE0ePHkXXrl0xb948uLq6\nonv37rCzs1M753r16oGI8PTpU5w9exa7du1C165dsWLFCpiamgq3A01MTFCvXj20a9cOgYGBaNWq\nFazyJr4xNTVFx44dYW1tjUmTJsHR0RHnzp3D/fv34ezsjIy8sc/W1tYwNTXV2i1wQ2eQPQQAUCgU\n6NmzJw4dOoSGDRvi448/RsOGDdGsWTPUqlULFhYWsLCwgEQiAaD8AQaU97a//vpr7NmzB7du3cKl\nS5dw8eJFnDt3DmvXrsXQoUMRFxcHQNnb6Nq1qyj5fvjhh7hz5w5q1KgBGxsbVKpUCTVr1kS1atWE\nXPOXbSvqr89XX30FAHjy5AmsrKzQoUMHPH/+HK9evQIAbNmyRZRc58+fjw4dOqBevXrIysrCpUuX\nULduXTRs2FDItVq1avj111/RuHFjAIBUKi20rZiYGJiZmaF169ZITU1F1apVceLECWzatAmAslck\nhqCgILRp0wb/+c9/0LRpU1haWqJZs2Zo1qyZkHPt2rWRkJAAAKhdu/Y7bbi6uuLYsWPYvHkzPDw8\ncPXqVYwZMwapqakAgOTkZOHnyGjp9abnG94ch3D58mUKDAykuXPnUkxMDO3du5eCg4PJx8eHvLy8\nCpyXnp5OFy9eJCKi2bNnE5FyBp/8r2fPnk1yuZxOnDhBEomEnJ2dyc/Pj4iI1q9fL7Qzf/58kr+x\nxHtJxyGEh4fTmTNn6Pjx4/Tnn3/SvXv3KCgoiB4+fEjff/99gfMUCgWdPXuWiIiOHDlCubm5NHfu\n3AK5EimXkL9+/TotX76czp07R0REsbGxQjurV6+muLg44evSjEOYP38+5eTk0KRJk4iIaN68ebRi\nxQrasGFDkcvP79mzh9LS0ig5OZkkEgn997//peTkZHJxcaHZs2dTeHg47du3j3bu3Em3b9+mI0eO\nCOfmrwx9/vx5unPnToF2SzoO4ejRo/TixQuaNm0aJSUl0U8//UTe3t7k7e1Nt27dKnBeQkICeXp6\nEhHRunXrKC0tjdatW0cymYxmz55NixcvJplMRseOHSOZTEZfffWV8PPy5ZdfEpFyXoeFCxcWaNdY\nxiEYZEEwBDwwSft4YJLhMdiPDIwx3eOCwBgTcEFgjAkM5mlHxpj+cQ+BMSbggsAYE3BBYIwJuCAw\nxgRcEBhjAi4IjDEBFwTGmIALAmNMwAWBMSbggsAYE3BBYIwJuCAwxgRcEBhjAi4IjDEBFwTGmIAL\nAmNMwAWBMSbggsAYE3BBYIwJuCAwxgRcEBhjAi4IjDEBFwTGmIALAmNMwAWBMSbggsAYE/w/7ar9\nRFz9HQkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1076b76d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xgb.plot_tree(bst, num_trees=1, yes_color='#FF0000', no_color='#0000FF')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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