diff --git a/clever_challenge.ipynb b/clever_challenge.ipynb new file mode 100644 index 0000000..d760817 --- /dev/null +++ b/clever_challenge.ipynb @@ -0,0 +1,5169 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "clever-challenge.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "AnuHgcPYmQRW", + "bLPjatkUmhkh", + "384J2g1i3lKY", + "-a8zqhzzeW-o", + "EfqXsdkENBvQ", + "lVqgPYkdeiJu", + "C6a0XgUlfTMC", + "MFtRS6hLM4ko", + "hWJsJOa5M6Yp", + "DAJVtMgLOMny" + ] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "metadata": { + "id": "a_gOOWakb7qk", + "colab_type": "code", + "outputId": "72c7025a-7595-496d-99fb-405d4a247173", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + } + }, + "cell_type": "code", + "source": [ + "!git clone https://github.com/MathieuNls/clever-challenge.git" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Cloning into 'clever-challenge'...\n", + "remote: Enumerating objects: 30, done.\u001b[K\n", + "remote: Counting objects: 3% (1/30) \u001b[K\rremote: Counting objects: 6% (2/30) \u001b[K\rremote: Counting objects: 10% (3/30) \u001b[K\rremote: Counting objects: 13% (4/30) \u001b[K\rremote: Counting objects: 16% (5/30) \u001b[K\rremote: Counting objects: 20% (6/30) \u001b[K\rremote: Counting objects: 23% (7/30) \u001b[K\rremote: Counting objects: 26% (8/30) \u001b[K\rremote: Counting objects: 30% (9/30) \u001b[K\rremote: Counting objects: 33% (10/30) \u001b[K\rremote: Counting objects: 36% (11/30) \u001b[K\rremote: Counting objects: 40% (12/30) \u001b[K\rremote: Counting objects: 43% (13/30) \u001b[K\rremote: Counting objects: 46% (14/30) \u001b[K\rremote: Counting objects: 50% (15/30) \u001b[K\rremote: Counting objects: 53% (16/30) \u001b[K\rremote: Counting objects: 56% (17/30) \u001b[K\rremote: Counting objects: 60% (18/30) \u001b[K\rremote: Counting objects: 63% (19/30) \u001b[K\rremote: Counting objects: 66% (20/30) \u001b[K\rremote: Counting objects: 70% (21/30) \u001b[K\rremote: Counting objects: 73% (22/30) \u001b[K\rremote: Counting objects: 76% (23/30) \u001b[K\rremote: Counting objects: 80% (24/30) \u001b[K\rremote: Counting objects: 83% (25/30) \u001b[K\rremote: Counting objects: 86% (26/30) \u001b[K\rremote: Counting objects: 90% (27/30) \u001b[K\rremote: Counting objects: 93% (28/30) \u001b[K\rremote: Counting objects: 96% (29/30) \u001b[K\rremote: Counting objects: 100% (30/30) \u001b[K\rremote: Counting objects: 100% (30/30), done.\u001b[K\n", + "remote: Compressing objects: 100% (26/26), done.\u001b[K\n", + "remote: Total 82 (delta 11), reused 18 (delta 4), pack-reused 52\u001b[K\n", + "Unpacking objects: 100% (82/82), done.\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "r-q8nO-bXigo", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import lightgbm as lgb\n", + "pd.set_option('display.max_columns',50)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "YWQfvIbud0zy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Peek sample and res" + ] + }, + { + "metadata": { + "id": "ZlyoQwajXvwN", + "colab_type": "code", + "outputId": "a6060635-ad2e-42f4-cea7-fab14a96b343", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1989 + } + }, + "cell_type": "code", + "source": [ + "pd.read_csv('clever-challenge/seq/sample.csv')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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0.012398 0.012233 0.016064 \n", + "f10 0.034839 0.016900 0.025125 \n", + "f12 0.121775 0.046162 0.057437 \n", + "f13 -0.014728 -0.008162 0.002808 \n", + "f14 0.227772 0.018598 0.016364 \n", + "f15 0.821281 0.403871 0.282733 \n", + "f17 1.000000 0.047092 0.061814 \n", + "f18 0.047092 1.000000 0.755423 \n", + "f20 0.061814 0.755423 1.000000 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 143 + } + ] + }, + { + "metadata": { + "id": "V3X7uR7AlTaM", + "colab_type": "code", + "outputId": "c84f2055-4535-404e-cfc8-ec5010528b2c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 629 + } + }, + "cell_type": "code", + "source": [ + "plt.figure(figsize=(25,10))\n", + "sns.heatmap(sample.corr())" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 154 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "bLPjatkUmhkh", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## strong corr in time & f8's max" + ] + }, + { + "metadata": { + "id": "TC9w29s6mtH6", + "colab_type": "code", + "outputId": "28f8680f-fc61-4440-b987-874f7160c5d1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 + } + }, + "cell_type": "code", + "source": [ + "sample.plot(x='timestamp',y='f8', figsize=(15,5),kind='scatter')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 205 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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PRnFGjQfLL5057OjbWO6RB5DIIwiCIAiCIDi6+sJ48NdbkCl8qCHqNEwRomiJ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PiVl4XH7/33xwRPHDLTWhCEc47Pvcjn2fq2pqzslWVKBBx8CfA1nxupFWWU9EquqF8cTqhyUh\n3XywcdVWHD7ZB29AOBE2GbSwmXU43Tt0JkSFIBjmsPb1vbDmwKvp9Yfh9gVhNWoTjDJ+ESZ+7I2f\nD3T0uvH0W+eMOY6L1inkF3Xl0GkgmBOIx6ASsx7v7Dwm+Ux7/CHsb+tJKX0hFZQUMuUIqQihLTHp\n0zJsLqgtTasmII/dGYz1mVgU7spLamTr+/a5/Jg2zoaDbkdBc4UHA40NlQnq5MnIR/pRtiAjbxCx\naUerxOqY9CuYj/BHOQGGdPKtcuF1UUuy4t5qQkvTbZecDDsA1Iww4fuLZynun+79yURQR9x/baf6\n8fjtjYo5W/HI/Z4Y8seoNlrE+3r8YRzvcEo+X+mGCrs8ARw/IwznYRgmKyUUMglfTuV9EhuEpRYD\njiPxvknh9gZRP7pE0hhVyjlhAGiALGZyZY9SqwEDLn9WhQRyYeBFawPmprh3MMypev9NBlYwPmo0\nALjkxnFPvw/T6kYkTNr1Og3KiovIyFOBwx1EdZn0xNGg0yhGjCjBl8+QGl/5XEj+N6koIilvHb9w\nGJAxSHU64bRSaoEr2XcsV/m6XhmFTCXUvO0ubyitnNrTdudZL336o+dzm/fi4HGhsXa8w4nDX/Qp\n7ufyhWGzGoZVUfR0WDBvPA4e61W9PcvkZ/6dLcjIyzMuTwA/f+eTWJyvjmXw0LKZmDDSJjnwTaq1\nweXxx0I2GQD3L74wL0mfagQYkpUtABLDI+PJh0BLsuLeakJLZdXHksAAKNJrEla5AeDoGfnj8R/c\nrj6h10jt/clmyGKfy5+VGHR+YOTDSjz+kMCjqmZfPp8vfhIgV8w9VTbtaE0oJtvn9OP7i6bHzpNu\nCQWpNqn10KXSl2KDsKG2OGlODo/ZqFMwRpXr2g1GAw8A/IFQ1pXicpE3MmqEGUdP5yZfCEj+/rs8\nAYRCwgmw0gKVYF9fKCZ6FV/awOEKDLqagYOZNhkBk3QNPJ5gmIPFqJX0EsYv3ojHDlYjvejAp2Ws\neX2vpPfqgjHC2rFXNtbERF10LIMrL6nBjl2n0vqeZkquorT7XH5o01ij6erzZRw6Luf9H3AHFcMQ\nK2xGRDhpQ/58YtWru1FbaVGcj8XDMMjL/DtbkJGXZzbtaBUkckZDNaKFT5ky/wAAGoRJREFUasWG\nRLFZB4ZhEIowaGyoLLgiH6DOKEsWHhlPPgValM7F577FlDlrbbjtunOF0vlt/3W4K6Wwr6njS9He\n7YE3IDWQMrFziyf74vw1cRH0ZGSzHAGQHQ+rXE0/NceWE1bh25uN3E6pdtgs+oS6iGpLKCRrk1oP\nXSp9KRZvSFYgl2WiddBKzPpYSG+8AMtzWw6gwmZE3chiNB9XXhkejIgLI2cDs5FNq4aeEqVWfUaT\nLbNBuVCwmpxXlz+9yabL48f3fvoPyd/cGdz/QokwFIpcKasyAMpLivBFd6Kwiz8QiikFisc/seop\nwwA2y7lcuwtqSxO8t0Y9i9uubRCMfe3drpghEwxz+Pm2g3h8RSPaTvUPK9GddEzxXOcGh7mohz4S\n4aBlAY8/mpVYYtbjyotr8IJCiaehhsnApjXeB8OcagMPAAoggJsRg8bIe/LJJ3HgwAEwDIOmpiZc\ndNFFhW5STpCaSPIvupQMPq+olk9FPiXEbbyysQb3vfB3OD1BaBgGU8aV4qYrJgKQD4+MJxWvS6YT\neaVziXPf9rXZoY3zXvH7PrZxF052nvtYxos5iDEaWPznv0/F02/uk/yY1Y2y4mdbD+CTIz2xYxzv\ncEqGyVSWGlPq+0zLEYg/wNn0sGbqvU03jDjVdpVaDQiFI7Hn4niHE8FQGKvv/ErSY0nl1/JtkitS\nL34/+O06etwotRpgNWlRVWqOXb/LE8Av/tCMlhOOqKFm0qcsLMDPMcaPLBbU/hEboLUjilI67nCm\nvMQIlzdRDCsTmo/1QcZxogolA0+N6FImizhBhXlVJIMaFeeTgZdLGMiXJQhziEVpSC3usQxQW2WV\n/N5KPTOBYAQPv/QR9FqNbN0xtzeaR3bXgqn4ySbpnLGhyGCtBBIzfOKcrg5XAD99ez9cviFmschg\nM+vRn0Ix80xQW3N0sDAojLzdu3fjxIkT2LJlC44cOYKmpiZs2bKl0M1SjWRhZ5ltpQZS7dmHJpkM\nvlLYY74Qr/SveX1vbKLIy8t6//sgjpx2JXyk7f0evPDbT9Dn9Ksy0qRyBJSM3t0Hz+Cl/26J/V03\n2or7bpgOcNFJ68kz/ejq94ND9OM1aYwNXn8YliIWn51IDHloPtaTUA+l3GoUGHlKA3uFzYBVG6QL\nkht0Gpzqckt+CD9p68GE0cK6Vic7nbjn+Z0xDyN/Td0OL4p0DD4/NRDrh8ljbVh+1QXw+gKxvLU9\nh7qgZRn84GxosBIWox6LLq/DS384dy97nF7BvZAzuI+ccmDNG3sFbbnz+mmx/T5t6xas/jIM0Hy0\nB89t3gedjk36bIgNG4fTD5cvmDSkMVm7AGnj8eGXPhQc59MjPRiI6zOpEidAVNRAvBK+51AXmp/7\nGww6VrLfO3vdgvd74x8PCWTzx1Vbz4ki9LixaoNQ7tzhDiDdz0/zkejYwl/7iTPCWlJf2H0ye55/\nnOhQNvDGqFTUFZOrSaI/kDwPSSw4lC0GoYjpsELDAFPGluLwyT7IRXVGAMXyGx09bnT0uCVzuCIc\nEA5H0Nbej2c274stMlmMeliK2ITtwxwHjz8EpUCHQCiCPYfP4K0dbckuj8ghw8XAAyC7oJALDHoW\nHX3uIVMQnX3sscceK3Qjtm7diunTp2Pq1KkoKyvDa6+9huuvvx56vfSHx+PJX4eq4bV3W7DnUBcc\nrgBO292w9/swb3atZDsnj7Xhj7tOCv6tSM/g2ksT1c8+O96H0/ZzBkU4wsWO39hQmf0LSQH+mqW+\n4b1O6f4JhDh09HoE90npOsT3dcAdQDBuZViv1WDejNGxvx/ZIDSK+5wB2Pt9+ORID/Yc6hKEDnEA\n7P0+OFwBdDl8ktcRCnMJbdz1WQc6VAoJ9LuD8MkoNoUjHHwyS+AcgJ4Bf8K/BcMRdPR6BNfkcAVg\nH/AL2m/v92Fvqx0nu4ThOREO+Ki5A9+8LLnS3qOvSd9L/l5IPfONDZX44csfCsIZ7P0+wX5Nv9iV\ncK5QmEOXw6fq2Xjt3Rbsb+uJXa8vGMbeVjtqKy2Cd2VijU2wf7J2AYBex6KxoRLzZoxGY0Ml9DoW\n7310UvDMcQC6ej2YOXFErD3NxxInR3KpSKGwfL+HwsL3e9P2w4JzD7gDuPbSsQCAVRv3wC0hIpCu\ndkeEA07b3bH7n0mY3flOf45kydMlKDGOidnd0oWONFRjidyj9EpzALocvowWCFhWg/9t7khQ3OUZ\n8ES/Y/3uoGB8+s1fj8h+35Lx8SE7/EMt7o0gEF2k2Ntqx1UXjyl0U2KYzQbZ3waFJ89ut2Pq1Kmx\nv8vKytDd3Q2LxSK5fWmpCVpt4ipSoRCvIvB/V1RYE7atQKJaVjjCSG5735LZWP/bA9jzWYdge4c7\nILl9PsnGykmy6xCfQyOKZ6qpsia9D5m2U9zGwTD5VXNNHp/0RDMU5lQ9O1Jzhvh7IfXMV1RYJfNK\n4vdTOxeRezakrt3jC8belc5eD6rKTLhz4XQUx5UDSNYuOS6sH4FdBzsE/9bZ65G9D9mAb5f4eddo\nzo0Tcv1rs+gRCEXgTlEmfDChFAJNpEeyZ92VhuogkV1GlptwpifR0NZnoK6phlKrAe3d6sOP+WfJ\nm6ECpoZhZItQE0Q+0WpSy7Xz+IIFn4OrZVAYeWK4JC9+X9/gWnG0iWpL8X93d0snc5oMOviD5zw1\npiKd7LYrrmmAP06FkD++3Pb5QnzN8ahNmE92HeJz1I8uFhQM//ZX65LeB6V2qkHcxkyPJ6bYrEMg\nEIJPJMuudA/VtEH8jPFoWUbVsyN1/vh7IfXMd3c7oZVQcozfL9NnQ+raTUU6+D1+QYFyv8cvEEdJ\n1i45ls6vR+uJPkF+YlWZSfY+xKNYjFZB2p9vV/3oYkGeaP3o4th5pfrXZtFj5ZKZgoLmahG3NV+i\nF1LnKTbpElROs42OZWAs0uasGPBgI9WxVi0mAwNPmoItxDl4L6uUkWcq0iEQ9Cu+j1LvkcmglSxF\nwIqMq/LiIvQN+CW/F1Lwz5LcN0Ytk8fYhqSYUzaZNs6Gk11uDHiyPw4lU1W2GFOvGThcsZoNcHkC\nqoVwlObshUDJ4BwU4ZoHDx4EwzCYNu2set769bj99tuHTLjm5LE22Pt90Gs1mFhjw/KrJqG0xCTb\nzukTy7G31Y5IhENxnKpdKsfX6wrryYxv07hqK0aWm1GkZzGxxoZF8+vwUbNwkqnRAA21JaittMa2\nS3Yd4uu+9ZoGXHbhSEE4XTyjKorw8aFzuQcTRlvxn/8+FdPry2Hv9yEcDMWSkFkGaBhrg8WoQ80I\nE3yBUGy1VMMARQYWF9aV45ZrLhCcZ/JYG77oHEBPvw9ggGKTFmMqzQkhqgyA266diBMdbsmQlqoy\nI6aMLcP9356OBXPr8fXZowXXumh+HT5u6Y55NIp0Guh1LKbVleGWay6IXZNeq0F1aRH6nOcmAZPH\n2nDnt6ahs8eNLse5XCo+J6/MmlxEY0KNBR/G9eGk2mLc8c0psXsh90xOqSvF/35yRtCW278xObbf\nhBqL4Nkw6DQw6FhcMKYEoyssSZ8N8f23WfR4aOnMpDmqydolh17H4rILqwXX+t1FMxE+G3I5eawN\nn3/Rm9D/FqMWK5fOgNMdgMPpQzgifK7u//Z0fGX6SOxttSMUCoPVMBhZbkbDmNLYtU8dX5bw/PPt\nFY8hj972JSy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zeDztHmM2h+Dn1/VvbWuxhHu7BOkk6rXvUK99i/rtO9Rr33E+vX5vdwkFBypo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\noRT4RERELgG3x8Oe4gr2HLDSYndha7K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VAgBCprzCq4xjRVqVdkAbv8syuxwAAJq9gMLarl27dMcddygvL08zZ85USUmJbDabJCk5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\nslJut1tt27Y1bZ9DWItwV199tTIyMjR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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "7x_Dwrsz1n7o", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Resource usage" + ] + }, + { + "metadata": { + "id": "JqydpEWmjWx8", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "res.set_index('event_id')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "384J2g1i3lKY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### f3 is res_counts" + ] + }, + { + "metadata": { + "id": "f_667ZudjQwt", + "colab_type": "code", + "outputId": "e25a777e-d1b9-46f1-ec50-aec9d5c93fb8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1071 + } + }, + "cell_type": "code", + "source": [ + "res['res_id'].value_counts()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "15552206 235\n", + "15450223 199\n", + "15548173 198\n", + "15543324 192\n", + "15456317 173\n", + "15577514 146\n", + "15548172 141\n", + "15551372 136\n", + "15548163 131\n", + "15557636 131\n", + "15450222 129\n", + "15574446 120\n", + "15543323 117\n", + "15557253 113\n", + "15575160 111\n", + "15565479 110\n", + "15457531 110\n", + "15548084 110\n", + "15551363 107\n", + "15565478 107\n", + "15552205 106\n", + "15457357 104\n", + "15576101 102\n", + "15457464 100\n", + "15452032 97\n", + "15449984 95\n", + "15513365 94\n", + "15544662 94\n", + "15459069 93\n", + "15556441 92\n", + " ... \n", + "15597804 1\n", + "15599853 1\n", + "15655152 1\n", + "15657201 1\n", + "15659250 1\n", + "15661299 1\n", + "15646964 1\n", + "15649013 1\n", + "15651062 1\n", + "15638776 1\n", + "15614180 1\n", + "15624417 1\n", + "15663308 1\n", + "15622368 1\n", + "15667406 1\n", + "15669455 1\n", + "15720656 1\n", + "15724754 1\n", + "15726803 1\n", + "15712468 1\n", + "15714517 1\n", + "15716566 1\n", + "15718615 1\n", + "15704280 1\n", + "15706329 1\n", + "15708378 1\n", + "15710427 1\n", + "15700190 1\n", + "15702239 1\n", + "15728640 1\n", + "Name: res_id, Length: 420219, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 86 + } + ] + }, + { + "metadata": { + "id": "FQ0MmH-biW7a", + "colab_type": "code", + "outputId": "d07d8259-73ca-4579-c5b0-bdc2a22bee68", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 399 + } + }, + "cell_type": "code", + "source": [ + "res.plot(x='event_id', y='res_id', kind='scatter', figsize=(15,5))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 94 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Sz9lazx01cl3052zF1eF0xXzn6HSrWrfQRMedaD8yzSui/mIwde7ygY90EhERDWBlJYak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\nGZIsUD0i8ANfolg+/OwwHvprT57EOx+r3beVD74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WKE2tXmbK6xzI7mBIDUZ4DYKylEHJ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3KqsNkIRmK4/efvKOpZVsTC53aiwu/CFxZOxs9f3YHg8CapyMPiG1fOwR/fOoTdR/qRAlDssePe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\ny8qIjKt6tbYb1rcz0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dTeCwBqXB5mf/l/00dw7YAbVQHWKovyV7XTD7VFV6j2p1r3GbFh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\nsu1WWY5rlM70lO7LlI779MqlvIflrK+9Q05bokAiq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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "8vuJU0Ba6PD5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Classifier\n", + "\n", + "- My original idea is to build a predictor and a classifier, the output of the predictor(prediction of features) as the input of classifier, but the performance of classifier is not good, thus killed this idea.\n", + "\n", + "- The first half of data has too many noise and a sharper trend in `event_counts` vs `timestamp`, so I tried to drop the first half, and classifier performance improved.\n", + "\n", + "- Tried to include time feature by translating `timestamp` into `minute`, `hour`, etc. They improved the classifier performance, please check the `feature importance` part." + ] + }, + { + "metadata": { + "id": "3nF_lSklVcUD", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "unixtime = pd.to_datetime(sample['timestamp'], unit='s')\n", + "sample = pd.concat([sample, unixtime.apply(lambda x: x.minute).rename('minute'), \n", + " unixtime.apply(lambda x: x.quarter).rename('quarter'), \n", + " unixtime.apply(lambda x: x.hour).rename('hour'), \n", + " unixtime.apply(lambda x: x.dayofweek).rename('dayofweek'), \n", + " unixtime.apply(lambda x: x.day).rename('day'), \n", + " unixtime.apply(lambda x: x.week).rename('week'), \n", + " unixtime.apply(lambda x: x.month).rename('month'), \n", + " unixtime.apply(lambda x: x.year).rename('year')], axis=1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "FSPiUvZg1u5S", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "tmp = sample.set_index('event_id').join(pd.DataFrame(res['event_id'].value_counts().rename('res_counts')))\n", + "tmp = tmp.loc[tmp['res_counts'].notnull()]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TzMEQcGjYdaY", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "unixtime = pd.to_datetime(tmp['timestamp'],unit='s')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "s8dXuSTmZx_K", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "tmp = pd.concat([tmp, unixtime.apply(lambda x: x.minute).rename('minute'), \n", + " unixtime.apply(lambda x: x.quarter).rename('quarter'), \n", + " unixtime.apply(lambda x: x.hour).rename('hour'), \n", + " unixtime.apply(lambda x: x.dayofweek).rename('dayofweek'), \n", + " unixtime.apply(lambda x: x.day).rename('day'), \n", + " unixtime.apply(lambda x: x.week).rename('week'), \n", + " unixtime.apply(lambda x: x.month).rename('month'), \n", + " unixtime.apply(lambda x: x.year).rename('year')], axis=1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "63Wz8lFyfOOX", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "tmp = tmp.loc[817020:]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "5Cz0YpP7f2C6", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "x_train, x_test, y_train, y_test = train_test_split(tmp.drop(['timestamp','res_counts','class'], axis=1), tmp['class'], random_state=1984)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-a8zqhzzeW-o", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## with time feature" + ] + }, + { + "metadata": { + "id": "4SXbqLvm6SJ3", + "colab_type": "code", + "outputId": "9fd54425-e0a3-4d75-da96-250cf37208be", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + } + }, + "cell_type": "code", + "source": [ + "model = lgb.LGBMClassifier(objective='binary')\n", + "model.fit(tmp.drop(['timestamp','res_counts','class'], axis=1),tmp['class'])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n", + " importance_type='split', learning_rate=0.1, max_depth=-1,\n", + " min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n", + " n_estimators=100, n_jobs=-1, num_leaves=31, objective='binary',\n", + " random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n", + " subsample=1.0, subsample_for_bin=200000, subsample_freq=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 27 + } + ] + }, + { + "metadata": { + "id": "jpzfrA6FcXjA", + "colab_type": "code", + "outputId": "f9f739a9-4de4-4a95-e3bd-d01b95b750df", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "cell_type": "code", + "source": [ + "pred = model.predict(tmp.drop(['timestamp','res_counts','class'], axis=1))\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "print(classification_report(tmp['class'], pred))\n", + "print(confusion_matrix(tmp['class'], pred))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.87 0.84 9992\n", + " 1 0.83 0.77 0.80 8316\n", + "\n", + " micro avg 0.82 0.82 0.82 18308\n", + " macro avg 0.83 0.82 0.82 18308\n", + "weighted avg 0.83 0.82 0.82 18308\n", + "\n", + "[[8729 1263]\n", + " [1944 6372]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "EfqXsdkENBvQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### feature importance" + ] + }, + { + "metadata": { + "id": "48-_Ve8Lc06A", + "colab_type": "code", + "outputId": "25da2b79-6184-4290-9aad-bbbdad83f9cd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + } + }, + "cell_type": "code", + "source": [ + "lgb.plot_importance(model, figsize=(15,5))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 30 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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UVY0abnTufK/59YEDn9GqlReenk1o0aLln8b30arVtXk1OnXqjLt7TQASEn7m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\nlZesxhMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "lVqgPYkdeiJu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## without time feature" + ] + }, + { + "metadata": { + "id": "I50-lpjz646w", + "colab_type": "code", + "outputId": "a735eab4-4e3b-4cdc-dacf-a8a375f4cda6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "cell_type": "code", + "source": [ + "pred = model.predict(tmp.drop(['timestamp','res_counts','class'], axis=1))\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "print(classification_report(tmp['class'], pred))\n", + "print(confusion_matrix(tmp['class'], pred))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.80 0.87 0.84 9992\n", + " 1 0.82 0.75 0.78 8316\n", + "\n", + " micro avg 0.81 0.81 0.81 18308\n", + " macro avg 0.81 0.81 0.81 18308\n", + "weighted avg 0.81 0.81 0.81 18308\n", + "\n", + "[[8670 1322]\n", + " [2104 6212]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "C6a0XgUlfTMC", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## later half data with time feature" + ] + }, + { + "metadata": { + "colab_type": "code", + "outputId": "3e141989-0e6e-4c2f-dc96-a9cc75e10aca", + "id": "HQLM1e8AfVXP", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + } + }, + "cell_type": "code", + "source": [ + "model = lgb.LGBMClassifier(objective='binary')\n", + "model.fit(x_train,y_train)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n", + " importance_type='split', learning_rate=0.1, max_depth=-1,\n", + " min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n", + " n_estimators=100, n_jobs=-1, num_leaves=31, objective='binary',\n", + " random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n", + " subsample=1.0, subsample_for_bin=200000, subsample_freq=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 44 + } + ] + }, + { + "metadata": { + "id": "MFtRS6hLM4ko", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### valid on train set" + ] + }, + { + "metadata": { + "colab_type": "code", + "outputId": "289e9f06-5ec5-4c52-ba00-3fd251f60a33", + "id": "Z30xXxXKfVXT", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "cell_type": "code", + "source": [ + "pred = model.predict(tmp.drop(['timestamp','res_counts','class'], axis=1))\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "print(classification_report(tmp['class'], pred))\n", + "print(confusion_matrix(tmp['class'], pred))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.84 0.89 0.86 6744\n", + " 1 0.86 0.80 0.83 5675\n", + "\n", + " micro avg 0.85 0.85 0.85 12419\n", + " macro avg 0.85 0.84 0.84 12419\n", + "weighted avg 0.85 0.85 0.85 12419\n", + "\n", + "[[5987 757]\n", + " [1150 4525]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "hWJsJOa5M6Yp", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### test on test set" + ] + }, + { + "metadata": { + "id": "r3Uc1wvWgwEy", + "colab_type": "code", + "outputId": "2eae339a-4a6e-4168-eb2f-88215c82cf95", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "cell_type": "code", + "source": [ + "pred = model.predict(x_test)\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "print(classification_report(y_test, pred))\n", + "print(confusion_matrix(y_test, pred))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.79 0.81 0.80 1678\n", + " 1 0.77 0.74 0.76 1427\n", + "\n", + " micro avg 0.78 0.78 0.78 3105\n", + " macro avg 0.78 0.78 0.78 3105\n", + "weighted avg 0.78 0.78 0.78 3105\n", + "\n", + "[[1367 311]\n", + " [ 368 1059]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "YZFbi0HqNUDo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Predictor\n", + "\n", + "Input are 10 timestamps of 25 features(original + `minute`, `hour`, etc.) extracted, input shape (-1, 10, 25)\n", + "\n", + "Output is the next `class` value.\n", + "\n", + "Evaluation Metric: Accuracy" + ] + }, + { + "metadata": { + "id": "S3GZJBYQB3zZ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Preprocessing\n", + "\n", + "Normalize and generate input." + ] + }, + { + "metadata": { + "id": "dSqo98PI-yXz", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "tmp = tmp.drop(['year', 'res_counts', 'timestamp'], axis=1)\n", + "class_0 = tmp.pop('class')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TaYgdUoR1gU4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "need_to_normalize = ['f2', 'f4', 'f5', 'f6', 'f8', 'f9', 'f10', 'f12', 'f13', 'f15', 'f17', 'f18', 'f20']\n", + "features_to_normalize = tmp[need_to_normalize].astype(np.float32)\n", + "for col in need_to_normalize:\n", + " features_to_normalize[col] /= np.max(features_to_normalize[col])\n", + "for col in features_to_normalize:\n", + " tmp[col] = features_to_normalize[col]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "cCRNULV48pXS", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def create_dataset(dataset_x, dataset_y, length_x, lentgh_y):\n", + " data_X = []\n", + " data_Y = []\n", + " head_index = 0\n", + " tail_index = head_index + length_x\n", + " while (tail_index + lentgh_y <= tmp.shape[0]):\n", + " data_x = dataset_x.iloc[head_index: tail_index].values\n", + " data_y = dataset_y.iloc[tail_index: tail_index + lentgh_y].values\n", + " data_X.append(data_x)\n", + " data_Y.append(data_y)\n", + " head_index += 1\n", + " tail_index += 1\n", + " #return np.array(data_X), np.array(data_Y)\n", + " return data_X, data_Y\n", + "x,y = create_dataset(tmp, class_0, 10, 1)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "S_e00eWW_8Gv", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "x = np.array(x)\n", + "y = np.array(y)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "YjzIOks4qorf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## LSTM\n", + "\n", + "Loss metric: validation set mean squared error\n", + "\n", + "Min `val_loss`: 0.2483, Accuracy: 0.545" + ] + }, + { + "metadata": { + "id": "W0aDdXNTqqj2", + "colab_type": "code", + "outputId": "02b30818-7102-4b38-fad2-0d31be0a4e35", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "cell_type": "code", + "source": [ + "from keras.layers import CuDNNLSTM, CuDNNGRU\n", + "from keras.layers import Dense\n", + "from keras.models import Sequential\n", + "\n", + "#callback\n", + "model = Sequential()\n", + "model.add(CuDNNLSTM(units = 100, input_shape=(10,25)))\n", + "model.add(Dense(1))\n", + "model.compile(loss=\"mse\", optimizer='adam')\n", + "model.summary()\n", + "\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "cu_dnnlstm_3 (CuDNNLSTM) (None, 100) 50800 \n", + "_________________________________________________________________\n", + "dense_4 (Dense) (None, 1) 101 \n", + "=================================================================\n", + "Total params: 50,901\n", + "Trainable params: 50,901\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "OxUK52HTsU6f", + "colab_type": "code", + "outputId": "9a9a3c79-620b-4cd0-ef6f-cab1a73579d1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 901 + } + }, + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from keras.callbacks import EarlyStopping\n", + "\n", + "callback = EarlyStopping(patience=15)\n", + "x_train, x_mid, y_train, y_mid = train_test_split(x, y, random_state=1984)\n", + "x_val, x_test, y_val, y_test = train_test_split(x_mid, y_mid, random_state=1984)\n", + "\n", + "model.fit(x = x_train,\n", + " y = y_train,\n", + " epochs = 100,\n", + " batch_size = 128,\n", + " callbacks = [callback],\n", + " validation_data = (x_val, y_val)\n", + " )" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Train on 13723 samples, validate on 3431 samples\n", + "Epoch 1/100\n", + "13723/13723 [==============================] - 2s 124us/step - loss: 0.3065 - val_loss: 0.2539\n", + "Epoch 2/100\n", + "13723/13723 [==============================] - 1s 65us/step - loss: 0.2480 - val_loss: 0.2495\n", + "Epoch 3/100\n", + "13723/13723 [==============================] - 1s 65us/step - loss: 0.2461 - val_loss: 0.2483\n", + "Epoch 4/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2432 - val_loss: 0.2492\n", + "Epoch 5/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2412 - val_loss: 0.2489\n", + "Epoch 6/100\n", + "13723/13723 [==============================] - 1s 67us/step - loss: 0.2394 - val_loss: 0.2535\n", + "Epoch 7/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2393 - val_loss: 0.2508\n", + "Epoch 8/100\n", + "13723/13723 [==============================] - 1s 65us/step - loss: 0.2403 - val_loss: 0.2490\n", + "Epoch 9/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2373 - val_loss: 0.2492\n", + "Epoch 10/100\n", + "13723/13723 [==============================] - 1s 65us/step - loss: 0.2365 - val_loss: 0.2482\n", + "Epoch 11/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2349 - val_loss: 0.2542\n", + "Epoch 12/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2350 - val_loss: 0.2491\n", + "Epoch 13/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2329 - val_loss: 0.2506\n", + "Epoch 14/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2315 - val_loss: 0.2505\n", + "Epoch 15/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2302 - val_loss: 0.2499\n", + "Epoch 16/100\n", + "13723/13723 [==============================] - 1s 65us/step - loss: 0.2295 - val_loss: 0.2513\n", + "Epoch 17/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2287 - val_loss: 0.2554\n", + "Epoch 18/100\n", + "13723/13723 [==============================] - 1s 65us/step - loss: 0.2262 - val_loss: 0.2514\n", + "Epoch 19/100\n", + "13723/13723 [==============================] - 1s 65us/step - loss: 0.2242 - val_loss: 0.2532\n", + "Epoch 20/100\n", + "13723/13723 [==============================] - 1s 67us/step - loss: 0.2237 - val_loss: 0.2586\n", + "Epoch 21/100\n", + "13723/13723 [==============================] - 1s 64us/step - loss: 0.2238 - val_loss: 0.2560\n", + "Epoch 22/100\n", + "13723/13723 [==============================] - 1s 65us/step - loss: 0.2205 - val_loss: 0.2571\n", + "Epoch 23/100\n", + "13723/13723 [==============================] - 1s 65us/step - loss: 0.2197 - val_loss: 0.2566\n", + "Epoch 24/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2169 - val_loss: 0.2605\n", + "Epoch 25/100\n", + "13723/13723 [==============================] - 1s 66us/step - loss: 0.2161 - val_loss: 0.2612\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 89 + } + ] + }, + { + "metadata": { + "id": "pRdanjZV4Ap3", + "colab_type": "code", + "outputId": "82a39f22-9020-4a9e-daa8-00bd7a0d1ad3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "logit = model.predict(x_test)\n", + "result = np.where(logit > 0.5, 1, 0)\n", + "result = result.reshape(-1,)\n", + "\n", + "\n", + "\n", + "#result.shape\n", + "y_test = y_test.reshape(-1,)\n", + "sum(result == y_test) / len(y_test)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.5454545454545454" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 92 + } + ] + }, + { + "metadata": { + "id": "8h_AuXZPB-SN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## GRU\n", + "\n", + "Min `val_loss`: 0.2510, Accuracy: 0.550" + ] + }, + { + "metadata": { + "id": "JHQA_qAQCNcu", + "colab_type": "code", + "outputId": "390913e7-8e98-4629-95f7-44a759377a01", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1054 + } + }, + "cell_type": "code", + "source": [ + "from keras.layers import CuDNNLSTM, CuDNNGRU\n", + "from keras.layers import Dense\n", + "from keras.models import Sequential\n", + "\n", + "#callback\n", + "model = Sequential()\n", + "model.add(CuDNNGRU(units = 100, input_shape=(10,25)))\n", + "model.add(Dense(1))\n", + "model.compile(loss=\"mse\", optimizer='adam')\n", + "model.summary()\n", + "\n", + "\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.callbacks import EarlyStopping\n", + "\n", + "callback = EarlyStopping(patience=15)\n", + "x_train, x_mid, y_train, y_mid = train_test_split(x, y, random_state=1984)\n", + "x_val, x_test, y_val, y_test = train_test_split(x_mid, y_mid, random_state=1984)\n", + "\n", + "model.fit(x = x_train,\n", + " y = y_train,\n", + " epochs = 100,\n", + " batch_size = 128,\n", + " callbacks = [callback],\n", + " validation_data = (x_val, y_val)\n", + " )\n", + "\n", + "import numpy as np\n", + "logit = model.predict(x_test)\n", + "result = np.where(logit > 0.5, 1, 0)\n", + "result = result.reshape(-1,)\n", + "\n", + "\n", + "\n", + "#result.shape\n", + "y_test = y_test.reshape(-1,)\n", + "sum(result == y_test) / len(y_test)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "cu_dnngru_1 (CuDNNGRU) (None, 100) 38100 \n", + "_________________________________________________________________\n", + "dense_5 (Dense) (None, 1) 101 \n", + "=================================================================\n", + "Total params: 38,201\n", + "Trainable params: 38,201\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Train on 13723 samples, validate on 3431 samples\n", + "Epoch 1/100\n", + "13723/13723 [==============================] - 2s 125us/step - loss: 0.3343 - val_loss: 0.2694\n", + "Epoch 2/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2565 - val_loss: 0.2581\n", + "Epoch 3/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2518 - val_loss: 0.2572\n", + "Epoch 4/100\n", + "13723/13723 [==============================] - 1s 60us/step - loss: 0.2471 - val_loss: 0.2535\n", + "Epoch 5/100\n", + "13723/13723 [==============================] - 1s 60us/step - loss: 0.2437 - val_loss: 0.2634\n", + "Epoch 6/100\n", + "13723/13723 [==============================] - 1s 60us/step - loss: 0.2425 - val_loss: 0.2661\n", + "Epoch 7/100\n", + "13723/13723 [==============================] - 1s 60us/step - loss: 0.2412 - val_loss: 0.2522\n", + "Epoch 8/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2374 - val_loss: 0.2524\n", + "Epoch 9/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2355 - val_loss: 0.2510\n", + "Epoch 10/100\n", + "13723/13723 [==============================] - 1s 60us/step - loss: 0.2356 - val_loss: 0.2566\n", + "Epoch 11/100\n", + "13723/13723 [==============================] - 1s 60us/step - loss: 0.2328 - val_loss: 0.2519\n", + "Epoch 12/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2324 - val_loss: 0.2631\n", + "Epoch 13/100\n", + "13723/13723 [==============================] - 1s 62us/step - loss: 0.2307 - val_loss: 0.2566\n", + "Epoch 14/100\n", + "13723/13723 [==============================] - 1s 60us/step - loss: 0.2296 - val_loss: 0.2547\n", + "Epoch 15/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2286 - val_loss: 0.2539\n", + "Epoch 16/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2247 - val_loss: 0.2616\n", + "Epoch 17/100\n", + "13723/13723 [==============================] - 1s 62us/step - loss: 0.2257 - val_loss: 0.2634\n", + "Epoch 18/100\n", + "13723/13723 [==============================] - 1s 62us/step - loss: 0.2243 - val_loss: 0.2590\n", + "Epoch 19/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2225 - val_loss: 0.2573\n", + "Epoch 20/100\n", + "13723/13723 [==============================] - 1s 60us/step - loss: 0.2214 - val_loss: 0.2579\n", + "Epoch 21/100\n", + "13723/13723 [==============================] - 1s 60us/step - loss: 0.2167 - val_loss: 0.2624\n", + "Epoch 22/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2185 - val_loss: 0.2557\n", + "Epoch 23/100\n", + "13723/13723 [==============================] - 1s 61us/step - loss: 0.2176 - val_loss: 0.2635\n", + "Epoch 24/100\n", + "13723/13723 [==============================] - 1s 59us/step - loss: 0.2163 - val_loss: 0.2633\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.5498251748251748" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 93 + } + ] + }, + { + "metadata": { + "id": "9O771Z20CoSb", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Bi-LSTM\n", + "\n", + "Min `val_loss`: 0.2489, Accuracy: 0.524" + ] + }, + { + "metadata": { + "id": "vuJgNLBACm2N", + "colab_type": "code", + "outputId": "46284a17-c3e1-496d-c0b1-cf97a7fe56c1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 952 + } + }, + "cell_type": "code", + "source": [ + "from keras.layers import CuDNNLSTM, CuDNNGRU, Bidirectional\n", + "from keras.layers import Dense\n", + "from keras.models import Sequential\n", + "\n", + "#callback\n", + "model = Sequential()\n", + "model.add(Bidirectional(CuDNNLSTM(units = 100), input_shape=(10,25), merge_mode='concat'))\n", + "model.add(Dense(1))\n", + "model.compile(loss=\"mse\", optimizer='adam')\n", + "model.summary()\n", + "\n", + "\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.callbacks import EarlyStopping\n", + "\n", + "callback = EarlyStopping(patience=15)\n", + "x_train, x_mid, y_train, y_mid = train_test_split(x, y, random_state=1984)\n", + "x_val, x_test, y_val, y_test = train_test_split(x_mid, y_mid, random_state=1984)\n", + "\n", + "model.fit(x = x_train,\n", + " y = y_train,\n", + " epochs = 100,\n", + " batch_size = 128,\n", + " callbacks = [callback],\n", + " validation_data = (x_val, y_val)\n", + " )\n", + "\n", + "import numpy as np\n", + "logit = model.predict(x_test)\n", + "result = np.where(logit > 0.5, 1, 0)\n", + "result = result.reshape(-1,)\n", + "\n", + "\n", + "\n", + "#result.shape\n", + "y_test = y_test.reshape(-1,)\n", + "sum(result == y_test) / len(y_test)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "bidirectional_3 (Bidirection (None, 200) 101600 \n", + "_________________________________________________________________\n", + "dense_7 (Dense) (None, 1) 201 \n", + "=================================================================\n", + "Total params: 101,801\n", + "Trainable params: 101,801\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Train on 13723 samples, validate on 3431 samples\n", + "Epoch 1/100\n", + "13723/13723 [==============================] - 2s 176us/step - loss: 0.2925 - val_loss: 0.2568\n", + "Epoch 2/100\n", + "13723/13723 [==============================] - 1s 103us/step - loss: 0.2472 - val_loss: 0.2539\n", + "Epoch 3/100\n", + "13723/13723 [==============================] - 1s 103us/step - loss: 0.2433 - val_loss: 0.2518\n", + "Epoch 4/100\n", + "13723/13723 [==============================] - 1s 105us/step - loss: 0.2400 - val_loss: 0.2508\n", + "Epoch 5/100\n", + "13723/13723 [==============================] - 1s 103us/step - loss: 0.2374 - val_loss: 0.2493\n", + "Epoch 6/100\n", + "13723/13723 [==============================] - 1s 103us/step - loss: 0.2366 - val_loss: 0.2489\n", + "Epoch 7/100\n", + "13723/13723 [==============================] - 1s 103us/step - loss: 0.2346 - val_loss: 0.2489\n", + "Epoch 8/100\n", + "13723/13723 [==============================] - 1s 103us/step - loss: 0.2332 - val_loss: 0.2503\n", + "Epoch 9/100\n", + "13723/13723 [==============================] - 1s 104us/step - loss: 0.2308 - val_loss: 0.2536\n", + "Epoch 10/100\n", + "13723/13723 [==============================] - 1s 104us/step - loss: 0.2291 - val_loss: 0.2511\n", + "Epoch 11/100\n", + "13723/13723 [==============================] - 1s 106us/step - loss: 0.2287 - val_loss: 0.2543\n", + "Epoch 12/100\n", + "13723/13723 [==============================] - 1s 103us/step - loss: 0.2242 - val_loss: 0.2590\n", + "Epoch 13/100\n", + "13723/13723 [==============================] - 1s 103us/step - loss: 0.2238 - val_loss: 0.2557\n", + "Epoch 14/100\n", + "13723/13723 [==============================] - 1s 106us/step - loss: 0.2211 - val_loss: 0.2570\n", + "Epoch 15/100\n", + "13723/13723 [==============================] - 1s 103us/step - loss: 0.2191 - val_loss: 0.2626\n", + "Epoch 16/100\n", + "13723/13723 [==============================] - 1s 102us/step - loss: 0.2163 - val_loss: 0.2619\n", + "Epoch 17/100\n", + "13723/13723 [==============================] - 1s 101us/step - loss: 0.2139 - val_loss: 0.2645\n", + "Epoch 18/100\n", + "13723/13723 [==============================] - 1s 102us/step - loss: 0.2109 - val_loss: 0.2638\n", + "Epoch 19/100\n", + "13723/13723 [==============================] - 1s 102us/step - loss: 0.2074 - val_loss: 0.2750\n", + "Epoch 20/100\n", + "13723/13723 [==============================] - 1s 104us/step - loss: 0.2054 - val_loss: 0.2681\n", + "Epoch 21/100\n", + "13723/13723 [==============================] - 1s 104us/step - loss: 0.2012 - val_loss: 0.2642\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.5236013986013986" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 97 + } + ] + }, + { + "metadata": { + "id": "QCMqOY2mCVUY", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## side note: predict using only `class` with lightGBM\n", + "\n", + "Use last 10 `class` to predict the next `class`." + ] + }, + { + "metadata": { + "id": "P1O_JXRH6Z8Y", + "colab_type": "code", + "outputId": "ba85c384-e344-4aa2-f7e8-60fa705bf0db", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + } + }, + "cell_type": "code", + "source": [ + "model = lgb.LGBMClassifier()\n", + "model.fit(class_0.iloc[:,:-1], class_0.iloc[:,-1])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n", + " importance_type='split', learning_rate=0.1, max_depth=-1,\n", + " min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n", + " n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n", + " random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n", + " subsample=1.0, subsample_for_bin=200000, subsample_freq=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 96 + } + ] + }, + { + "metadata": { + "id": "oQa0P18TFrId", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### valid on train set" + ] + }, + { + "metadata": { + "id": "kQACIQ9n6v1g", + "colab_type": "code", + "outputId": "cc257b8a-14df-4d98-8fa5-12525e578172", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 187 + } + }, + "cell_type": "code", + "source": [ + "pred = model.predict(class_0.iloc[:,:-1])\n", + "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", + "print(classification_report(pred, class_0.iloc[:,-1]))\n", + "print(accuracy_score(pred, class_0.iloc[:,-1]))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.79 0.83 11125\n", + " 1 0.71 0.83 0.77 7083\n", + "\n", + " micro avg 0.80 0.80 0.80 18208\n", + " macro avg 0.80 0.81 0.80 18208\n", + "weighted avg 0.82 0.80 0.81 18208\n", + "\n", + "0.8039323374340949\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "yjtulTa2FuSQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### test on test set: Accuracy=0.7\n", + "\n", + "The original output was mistakenly deleted. This model performed best." + ] + } + ] +} \ No newline at end of file