Added desicion tree to info
This commit is contained in:
Jany committed
1 parent
e329515d79
commit
94c7df0712
1 file changed
+94
-33
+94
-33
@@ -2,19 +2,71 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 10,
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"id": "b6ea6c3b",
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"metadata": {},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "'numpy.ndarray' object is not callable",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mTypeError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[25]\u001b[39m\u001b[32m, line 20\u001b[39m\n\u001b[32m 18\u001b[39m X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=\u001b[32m0.2\u001b[39m, random_state=\u001b[32m42\u001b[39m)\n\u001b[32m 19\u001b[39m X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=\u001b[32m0.2\u001b[39m, random_state=\u001b[32m42\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m X_train = \u001b[43mX_train\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 23\u001b[39m n_features = \u001b[32m10\u001b[39m\n\u001b[32m 24\u001b[39m fig=plt.figure( figsize=(\u001b[32m15\u001b[39m, \u001b[32m15\u001b[39m) )\n",
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"\u001b[31mTypeError\u001b[39m: 'numpy.ndarray' object is not callable"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" age workclass education.num marital.status occupation \\\n",
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"0 90 0 9 6 0 \n",
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"1 82 4 9 6 4 \n",
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"2 66 0 10 6 0 \n",
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"3 54 4 4 0 7 \n",
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"4 41 4 10 5 10 \n",
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"... ... ... ... ... ... \n",
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"32556 22 4 10 4 11 \n",
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"32557 27 4 12 2 13 \n",
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"32558 40 4 9 2 7 \n",
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"32559 58 4 9 6 1 \n",
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"32560 22 4 9 4 1 \n",
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"\n",
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" relationship race sex capital.gain capital.loss hours.per.week \n",
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"0 1 4 0 0 4356 40 \n",
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"1 1 4 0 0 4356 18 \n",
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"2 4 2 0 0 4356 40 \n",
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"3 4 4 0 0 3900 40 \n",
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"4 3 4 0 0 3900 40 \n",
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"... ... ... ... ... ... ... \n",
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"32556 1 4 1 0 0 40 \n",
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"32557 5 4 0 0 0 38 \n",
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"32558 0 4 1 0 0 40 \n",
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"32559 4 4 0 0 0 40 \n",
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"32560 3 4 1 0 0 20 \n",
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"\n",
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"[32561 rows x 11 columns]\n"
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]
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAB1AAAAN8CAYAAADmrDj+AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XV0FNcXwPHvStyFCElwd3d3dyvSAoXS0gKlSIVfFdpSWlooBQpV3N3dAwRNgiXE3d2T3f39sWQhZDdGIAl9n3NyTrLz3szdie3OnXufRKVSqRAEQRAEQRAEQRAEQRAEQRAEQRAEQRCQl3UAgiAIgiAIgiAIgiAIr4vk5GQOHz7M2bNniY2NJT0traxDEgThP0Kup4elpSUNGzZk1KhR1KpVq6xDEgRBEIQKSyIqUAVBEARBEARBEARBEF5MTEwM786YwZGjR8jIyKRBVQccLI0x0pcjkZR1dIIg/Bdk5yhJSMvEMyCStIwsmjdrynffL6Vfv35lHZogCIIgVDgigSoIgiAIgiAIgiAIgvACYmJi6NG9GxEhgcwa1Joh7erhUsmirMMSBOE/Ki0zm9N3fPnr5B3cHoexZ89eBg0aVNZhCYIgCEKFIhKogiAIgiAIgiAIgiAIJaRQKGjZojlhQf7s/3wc9ZxtyzokQRAEALJyFExbeZBTd/04f/4C7du3L+uQBEEQBKHCkJZ1AIIgCIIgCIIgCIIgCBXVxYsXcffw5O85Q0TyVBCEckVfLuPPOUNwsTVn/fr1ZR2OIAiCIFQoIoEqCIIgCIIgCIIgCIJQQrt27cLFzor29V3KOhRBEIR89OUyhrWry/59e8nKyirrcARBEAShwhAJVEEQBEEQBEEQBEEQhBLav28vg1vXQiKRlHUogiAIWg1tV4+ExCQuXrxY1qEIgiAIQoUhEqiCIAiCIAiCIAiCIAglkJOTQ3hEJPVcROteQRDKrzpONgAEBweXcSSCIAiCUHGIBKogCIIgCIIgCIIgCEIJJCcnA2BmZFDGkQiCIOimJ5dhbKhPYmJiWYciCIIgCBWGSKAKgiAIgiAIgiAIgiCUgEqlAkAqfT3b924974HN2KU0+2DNf/L4peny/UBsxi7FZuzSMpkvCDKpVPM3SxAEQRCEwsnLOgBBEARBEARBEARBEAThvyMoKoFtFzwB+Hh05zKORvivSU7PZM1hNw5d9yYoKgGpVEpNRytGdKjP9P6t0JfLSrTfH3ZdYtnuK4WOu7FyBjUcrEp0DEEQBEEQXh2RQBUEQRAEQRAEQRAEQRBemaDoRE2iqaAEqrmxAbUqW+NobfaqQiu3jAz0qFXZuqzDqPCCoxMZ8vVWgqLVrWyNDfTIysnhrl8Ed/0i2H35Afs+fwNLU8MSH0NPJsXK1EjndvlrWrEuCIIgCK8bkUAVBEEQBEEQBEEQBEEQyp1BbeoyqE3dsg6jXGhZqzLXf3mnrMOo0HIUSsYv201QdCL2VqasmTmIbk2qoVSqOHDtER+uP4ZHQCTv/naI7Z+MLvFx2tR14uCXE0oxckEQBEEQyoJYA1UQBEEQBEEQBEEQBEEQhFL3KCSGG96hZR0GANsvePIgKBqADR8Np1uTaoB6DePhHerz8/R+AJy648sFz4AyilIQBEEQhPJCVKAKgiAIgiAIgiAIgiBUMEO+3sKVB8EsHNWRj4Z3YP2xm+xxfYh/RDxJaZkc+OINOjWsqhn/MCia9cdvcvl+EOFxyUglEqraW9K3ZS3eG9AaG3PjYh0/O0fB6bt+nLztg7t/JBFxycQlp2NhYkjjava80a0xIzrURyLJ26602QdrCI5O0nxtM3Zpnu3jujZi9cxBAGw978GstUdxqWTO3d9mao3DPyKe3w67cdEzgLDYZORyKTXsrRjQujYzBrTG3Ngg35zL9wMZ+s02AGJ3fIJfRDy/7HPlvGcAMYlp2Jgb06tZDRaO7kRlHe2DvUNjWXvEjSsPggiLTUapUmFjboyjlRmdG1VlbJdG1HGy0Xn+invM52N+1vPn6ZyHP2uP3OCubzipGdlUtbdkRIf6fDC4LYb6L/9SYHRiKnsuP2DnpXu4+0eycFRHWtdxeunHLcz2J+vudmpYRWs8IzrU59vtFwiMSmTHxXt0bVztFUcoCIIgCEJ5IhKogiAIgiAIgiAIgiAIFVRGtoIhX2/FzTsUuUyKqaE+z+Us+fXANRZvu4BSpQLU6z5mK5Q8CIrmQVA0W897sv3jUTSp7lDk4173CmHij3s0X5sZGWCgLycmKY1zHv6c8/DniJs3f84ZivSZNR9tzIxJTssiITUDADsLkzz71Zbw1GX/1YfMXH2YzGwFAKZG+mTnKPAIiMQjIJJNZz3Y9dkY6jrb6tzHpXuBTPhxD6kZWZga6aNUqQiPS2bTWXdO3fHl1Hdv5UtonvPwZ8Ky3Zrj6smkGBvqERabTFhsMrd8wtCXS3Wu71qSYxbVXydu8/E/J1GpwMLEgBylEq+QGL7feYkjbt4vvL6nLulZ2Ry78Zidl+5zzsOfHIUSAH25DCcb81I/XnGlZWZz3UtdCdurWU2tYyQSCT2a1uCfU3c47+H/KsMTBEEQBKEcEglUQRAEQRAEQRAEQRCECuqvE7cBWPXeAIZ3qI+Rvh5xyemaJOrms+58vfU8Job6fDisHRO6N8He0hSFUolnQBRfbTmnSehd/Xk6pob6RTqukYEek3s1Y3DberSo5ahJfManpLPz0n2+33GJA9ce0baeMzP6t9LMO/P95DzVlA/XzyrR83b3i+DdVYfIVihpW9eZn6b1oUEVO5RKFSfv+PDRHycIjU1iwrLdnF82VefzmvzzPjo3qsKX47tTx8mGrBwFR294M2fdMSLiU1i89TxrPxicZ86CP0+Qma2ge5PqLJ7Ug/pVKgGQkZVDQGQ8h6574WSrO2lYkmMWRWxSOos2nGZI23osntQDJ1tz0rOy2X7hHos2nMYjIJLZvx9l4/wRxd63NiqVisv3g9h56R4Hr3uRkp4FgEQC7es5M7pzI4a2q/dSErbF5R0aq7mBoL6L7oR6fRf19zIyIZX4lHSsTI2KfaxHwTF0nPcngVEJSCUSHKzN6FDfhal9mhfrJgVBEARBEMqWSKAKgiAIgiAIgiAIgiBUUKkZWWxZOJJ+LWtrHrM2Uyd9ktMz+WLzWQD+nTuMHs1qaMbIpFKa1XBg92dj6fO/jbj7RbD5jDvvDmxdpOO2rFWZlrUq53vcytSIGf1b4WhlypRf9vPHsZt5Eqil5dsdF8lWKKnhYMXuRWMxNtAD1OtZ9mtZm8rWZvRetBH/yAT+PXWHDwa31bqfRtXs2DRvpKZKVl8uY1j7+kQlpPLpv6c5dN2LVe8NRC6TAur2tP6RCQD8NnMgDlammn0Z6sup51KJek+ScLoU95hFlZaZTccGLnmqfo309ZjSuzl6Milz1h3jyA1vbvuE06KWY7H2/SyvkBh2XrrPrkv3CY192o65vkslRnVqwKhODXC2tShwH88m0Uti1XsDGN+tSZHHR8Qnaz53LKC619H66fczIi6lRAnU2OR04lMysDAxIDk9C9/wOHzD49h8zp25wzqwaFyXYu9TEARBEIRXTyRQBUEQBEEQBEEQBEEQKqh6zrZ5kqfPOnTdi8TUTJpUs8+TPH2WXCZlZIf6uPtFcNbDv8gJ1ML0bqFuk+ofmUBkQgr2lqaFzCi6xNQMzrr7AfDB4Laa5OmzmlR3YFCbOuy/+oi9Vx7oTKDOHdYhT4vhXANa1+bTf0+TnpWDb3icpg2wqZE+UokEpUpFZHxKngRqURX3mMXx0fCOWvc9vlsTfth9mbDYZPa5Pih2AjU6MZW9Vx6w49J93P0iNI9XtjFjZAd10rRRNfsi709fLsvXvrk4jPTzf88LklsdC+rqaZ37fWZbSkaWznHa1HCw5qsJ3enfqjZV7SzQk8vIylFw5X4Qi7dfwN0vgp/3uWJpYsD7On4eBUEQBEEoP0QCVRAEQRAEQRDKgFKp5OrVq+zatYuzZ88QGxtLWlp6WYclCILw0kkkEiwszHFxcWHo0GGMGjWKqlWrlnVYFVbbus46t7k9WfPROzSW+u+s0jkuPSsHgODoxGIdOzk9k39P3eXkbR+8Q2NJTM0g+8nal88Ki00u1QSqu38ET7qx0rVxNZ3jujWuzv6rj7gfFE12jgI9uSzfmJa1tScSHayeVikmpGRoPjfS16NL46qc9whgzPc7mdy7Gb2b16JJdXv0texfm+Ies6jkMint62v/eZBKJXRsUIVdl+5z95kEaFGExSXT/IO1mnVNLUwMGNymLqM6NaRTwypInl90twja1HUucfvm8mp054b5HtOXy+jetDrt67sw6Kst3PEN54fdV5jUsynmxmXf2vh1plAouHLlCrt27eL8k9fa6RnF/70SBEEobYYGBlhbW9Gxc1dGjx5N9+7dkctFqq48Et8VQRAEQRAEQXjFPDw8GDx4MEFBQTja2zGgVzccHewwNjIq0UVIQRCEikShUJCUnMIDbx8WLVrE/PnzGTp0CNu2bcfIqPjtMv/rbC2MdW6LiE8BICM7h4zEnEL3lZ6VXeTj+oTFMXzJNsJin7ZGNTbQw8LEEOmT/2VRiamAurVsaYpJTNN8/mzL1edVtlEnJHMUSuJTMrCzzF/xaGZkoHXus+1zsxWKPNtWzujPhGV7uBcYxU97XPlpjyv6chnNazrSv1VtJvZoUmDr15IcsyhszIww0NN9qS+3dW30M+evKHJyFJrkKUCvZjUZ3qEBHRuULHlaFkyNnq6Bm17Az+Oz24q6HnBRGOrL+d+4roz8djupGVlc8AxkcNu6pbZ/Ia+bN28ybOgQQsPCcbQyoVc9W+xcrDDSk1FBfmQFQXiNZWQriU7O4NT+7fzxxx/Y2lizfcdOevbsWdahCc8RCVRBEARBEARBeIU8PDzo0aMHVZwc+HflFjq0aYlUWrw1zgRBEF4XSckp7D18nDmLvmHLine truncated
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"text/plain": [
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"<Figure size 2000x1000 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Feature Importances:\n",
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" feature importance\n",
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"5 relationship 0.218174\n",
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"0 age 0.174282\n",
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"2 education.num 0.147966\n",
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"8 capital.gain 0.129988\n",
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"10 hours.per.week 0.105884\n",
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"4 occupation 0.085228\n",
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"1 workclass 0.052566\n",
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"9 capital.loss 0.044720\n",
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"6 race 0.023650\n",
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"3 marital.status 0.011255\n",
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"7 sex 0.006286\n"
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]
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}
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],
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@@ -24,42 +76,51 @@
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"import matplotlib.pyplot as plt\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"\n",
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"df = pd.read_csv('./Datasets/adult.csv', comment = '#')\n",
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"\n",
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"# First, create a copy of the dataframe to avoid modifying the original\n",
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"df_encoded = df.copy()\n",
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"\n",
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"data = pd.read_csv('./Datasets/adult.csv', comment = '#')\n",
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"# Apply label encoding to categorical columns\n",
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"label_encoder = LabelEncoder()\n",
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"categorical_columns = ['workclass', 'marital.status', 'occupation', \n",
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" 'relationship', 'race', 'sex', 'income']\n",
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"\n",
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"# Features\n",
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"X = data.drop(columns=['income'])\n",
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"\n",
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"# Labels\n",
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"y = data['income']\n",
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"for column in categorical_columns:\n",
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" df_encoded[column] = label_encoder.fit_transform(df_encoded[column])\n",
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"\n",
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"# Now properly separate features and target\n",
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"X = df_encoded.drop(columns=['income', 'native.country', 'education','fnlwgt'])\n",
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"y = df_encoded['income']\n",
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"\n",
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"# Split the data\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
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"X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
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"\n",
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"print(X)\n",
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"\n",
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"# Create and train the decision tree classifier\n",
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"clf = DecisionTreeClassifier(random_state=42)\n",
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"clf.fit(X, y)\n",
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"\n",
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"n_features = 10\n",
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"fig=plt.figure( figsize=(15, 15) )\n",
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"plt_num = 1\n",
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"for i in range(n_features):\n",
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" for j in range(n_features):\n",
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" ax = fig.add_subplot(n_features, n_features, plt_num)\n",
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" if(i == j):\n",
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" ax.hist(X_train[:, i], bins=25, color='gray')\n",
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" else:\n",
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" ax.scatter(X_train[:, j], X_train[:, i], c=np.array(colors)[y_train], s=30, alpha=0.3)\n",
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"# Visualize the tree\n",
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"plt.figure(figsize=(20,10))\n",
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"plot_tree(clf, feature_names=X.columns, class_names=['No', 'Yes'], \n",
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" filled=True, rounded=True, fontsize=18)\n",
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"plt.title(\"Rod Breaking Decision Tree\",fontsize=30)\n",
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"plt.show()\n",
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"\n",
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" if(i == n_features-1):\n",
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" ax.set_xlabel(f'$x_{{{j}}}$', fontsize=22)\n",
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" \n",
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" if(j==0):\n",
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" ax.set_ylabel(f'$x_{{{i}}}$', fontsize=22)\n",
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"\n",
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" ax.grid(True)\n",
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" plt_num +=1\n"
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"# Print feature importances\n",
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"importances = pd.DataFrame({\n",
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" 'feature': X.columns,\n",
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" 'importance': clf.feature_importances_\n",
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"})\n",
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"print(\"\\nFeature Importances:\")\n",
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"print(importances.sort_values('importance', ascending=False))\n",
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"\n"
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]
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}
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],
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Reference in new issue
Block a user