iui-group-l-name-zensiert/1-first-project/ies/Testor.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "90c12c6e",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"from math import isqrt\n",
"import pickle\n",
"from tqdm import tqdm\n",
"delim = ';'\n",
"\n",
"user_count = 100\n",
"base_path = '/opt/iui-datarelease2-sose2021/'\n",
"Xpickle_file = './X.pickle'\n",
"ypickle_file = './y.pickle'\n",
"xshorted_pickle_file = './X_shorted.pickle'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6ab6f75a",
"metadata": {},
"outputs": [],
"source": [
"def load_pickle():\n",
" _p = open(xshorted_pickle_file, 'rb')\n",
" X = pickle.load(_p)\n",
" _p.close()\n",
" \n",
" \n",
" \n",
" return (np.asarray(X, dtype=pd.DataFrame))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "85107f4e",
"metadata": {},
"outputs": [],
"source": [
"def load_pickle_Y():\n",
"\n",
" _p = open(ypickle_file, 'rb')\n",
" y = pickle.load(_p)\n",
" _p.close()\n",
" \n",
" return ( np.asarray(y, dtype=str))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a3f2395b",
"metadata": {},
"outputs": [],
"source": [
"f_data = load_pickle()\n",
"y = load_pickle_Y()\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "23fb2642",
"metadata": {},
"outputs": [
{
"data": {
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"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": [
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" [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",
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" ...\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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]
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fd08cbc1550>]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"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
}