295 lines
81 KiB
Plaintext
295 lines
81 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "90c12c6e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"import matplotlib.pyplot as plt\n",
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"from math import isqrt\n",
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"import pickle\n",
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"from tqdm import tqdm\n",
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"delim = ';'\n",
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"\n",
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"user_count = 100\n",
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"base_path = '/opt/iui-datarelease2-sose2021/'\n",
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"Xpickle_file = './X.pickle'\n",
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"ypickle_file = './y.pickle'\n",
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"xshorted_pickle_file = './X_shorted.pickle'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "6ab6f75a",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_pickle():\n",
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" _p = open(xshorted_pickle_file, 'rb')\n",
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" X = pickle.load(_p)\n",
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" _p.close()\n",
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" \n",
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" \n",
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" \n",
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" return (np.asarray(X, dtype=pd.DataFrame))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "85107f4e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_pickle_Y():\n",
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"\n",
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" _p = open(ypickle_file, 'rb')\n",
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" y = pickle.load(_p)\n",
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" _p.close()\n",
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" \n",
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" return ( np.asarray(y, dtype=str))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "a3f2395b",
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"metadata": {},
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"outputs": [],
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"source": [
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"f_data = load_pickle()\n",
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"y = load_pickle_Y()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "23fb2642",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"for i in range(0,len(f_data)):\n",
|
|
" plt.plot(*f_data[i])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "c5eef3e2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[list([108.0, 362.0, 683.0, 994.0, 1306.0, 1380.0, 1526.0, 1724.0, 1895.0, 1985.0, 2077.0, 2040.0, 1997.0, 1917.0, 1623.0, 1357.0, 1087.0, 785.0, 450.0, 146.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 67.0, 615.0, 927.0, 1212.0, 1450.0, 1483.0, 1546.0, 1634.0, 1617.0, 1691.0, 1654.0, 1663.0, 1618.0, 1550.0, 1519.0, 1519.0, 1742.0, 1892.0, 1966.0, 1966.0, 2092.0, 2040.0, 2048.0, 1997.0, 1953.0, 1946.0, 1926.0, 1791.0, 1497.0, 1112.0, 678.0, 279.0, 0])]\n",
|
|
" [list([464.0, 587.0, 824.0, 1016.0, 1156.0, 1285.0, 1454.0, 1603.0, 1728.0, 1851.0, 1921.0, 1963.0, 1952.0, 1872.0, 1742.0, 1631.0, 1641.0, 1629.0, 1628.0, 1668.0, 1696.0, 1765.0, 1803.0, 1879.0, 1879.0, 1859.0, 1812.0, 1738.0, 1683.0, 1678.0, 1698.0, 1722.0, 1741.0, 1783.0, 1769.0, 1758.0, 1752.0, 1750.0, 1710.0, 1668.0, 1673.0, 1694.0, 1705.0, 1687.0, 1594.0, 1447.0, 1398.0, 1303.0, 1220.0, 1172.0, 1183.0, 1203.0, 1280.0, 1364.0, 1420.0, 1433.0, 1409.0, 1386.0, 1490.0, 1658.0, 1725.0, 1807.0, 1869.0, 1904.0, 1929.0, 1918.0, 1888.0, 1847.0, 1777.0, 1543.0, 1232.0, 812.0, 356.0, 0])]\n",
|
|
" [list([403.0, 534.0, 734.0, 931.0, 1133.0, 1235.0, 1317.0, 1391.0, 1434.0, 1499.0, 1438.0, 1423.0, 1396.0, 1407.0, 1445.0, 1412.0, 1443.0, 1461.0, 1475.0, 1504.0, 1535.0, 1469.0, 1255.0, 1044.0, 794.0, 554.0, 236.0, 0])]\n",
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" ...\n",
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" [list([1416.0, 1265.0, 1542.0, 1964.0, 2060.0, 2060.0, 2010.0, 2035.0, 1993.0, 1935.0, 1935.0, 1949.0, 1844.0, 1651.0, 1571.0, 1554.0, 1574.0, 1496.0, 1357.0, 1045.0, 701.0, 348.0, 94.0, 7.0, 10.0, 12.0, 14.0, 11.0, 29.0, 105.0, 783.0, 970.0, 1294.0, 1389.0, 1514.0, 1479.0, 1439.0, 1055.0, 557.0, 43.0, 11.0, 12.0, 16.0, 17.0, 17.0, 19.0, 19.0, 19.0, 16.0, 16.0, 16.0, 17.0, 16.0, 15.0, 15.0, 13.0, 14.0, 14.0, 17.0, 18.0, 16.0, 17.0, 16.0, 16.0, 13.0, 17.0, 17.0, 16.0, 14.0, 17.0, 15.0, 13.0, 15.0, 17.0, 15.0, 14.0, 13.0, 16.0, 14.0, 15.0, 15.0, 14.0, 16.0, 15.0, 51.0, 665.0, 615.0, 779.0, 939.0, 975.0, 1042.0, 1129.0, 1195.0, 1271.0, 1298.0, 1314.0, 1289.0, 1238.0, 1076.0, 865.0, 562.0, 209.0, 0])]\n",
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" [list([847.0, 707.0, 927.0, 1026.0, 1148.0, 1299.0, 1391.0, 1462.0, 1512.0, 1549.0, 1568.0, 1610.0, 1607.0, 1807.0, 2033.0, 2194.0, 2310.0, 2420.0, 2606.0, 2588.0, 2585.0, 2581.0, 2587.0, 2581.0, 2562.0, 2503.0, 2290.0, 1634.0, 1058.0, 607.0, 141.0, 0])]\n",
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" [list([656.0, 729.0, 1036.0, 1253.0, 1465.0, 1662.0, 1707.0, 1730.0, 1897.0, 1890.0, 1885.0, 1856.0, 1833.0, 1789.0, 1746.0, 1752.0, 1766.0, 1799.0, 1780.0, 1759.0, 1743.0, 1732.0, 1735.0, 1824.0, 1788.0, 1737.0, 1637.0, 1547.0, 1443.0, 1199.0, 862.0, 400.0, 127.0, 0])]]\n",
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"[1 1 1 ... 1 1 1]\n",
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"[1.0]\n",
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"[1.0, 1.0]\n",
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"[1.0, 1.0, 1.0, 1.0, 1.0]\n",
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"[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
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"[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
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"[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
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lqREGXZIaYdAlqREGXZIa8b9KhXRYrthEUwAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"\n",
|
|
"X_len = np.asarray(list(map(len, f_data)))\n",
|
|
"\n",
|
|
"\n",
|
|
"l = []\n",
|
|
"sq_xlen = pd.Series(X_len)\n",
|
|
"ptiles = [x*0.01 for x in range(100)]\n",
|
|
"for i in ptiles:\n",
|
|
" l.append(sq_xlen.quantile(i))\n",
|
|
" print(l)\n",
|
|
"\n",
|
|
"sq_xlen.describe(percentiles=[x*0.01 for x in range(95,100)])\n",
|
|
"\n",
|
|
"\n",
|
|
"threshold_p = 0.99\n",
|
|
"threshold = int(sq_xlen.quantile(threshold_p))\n",
|
|
"len_mask = np.where(X_len <= threshold)\n",
|
|
"\n",
|
|
"X_filter = f_data[len_mask]\n",
|
|
"y_filter = y[len_mask]\n",
|
|
"X_filter.shape, X_filter[0].shape\n",
|
|
"\n",
|
|
"plt.plot(l, ptiles)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a4399c56",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"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": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|