Add lag trend notebook

execution/lag-trend-graph.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "directory = ''\n",
+    "filename = 'xxx_totallag.csv'\n",
+    "warmup_sec = 60\n",
+    "threshold = 2000 #slope"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(os.path.join(directory, filename))\n",
+    "\n",
+    "input = df.iloc[::3]\n",
+    "#print(input)\n",
+    "input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']\n",
+    "#print(input)\n",
+    "#print(input.iloc[0, 'timestamp'])\n",
+    "regress = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up\n",
+    "#regress = input\n",
+    "\n",
+    "#input.plot(kind='line',x='timestamp',y='value',color='red')\n",
+    "#plt.show()\n",
+    "\n",
+    "X = regress.iloc[:, 4].values.reshape(-1, 1)  # values converts it into a numpy array\n",
+    "Y = regress.iloc[:, 3].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
+    "linear_regressor = LinearRegression()  # create object for the class\n",
+    "linear_regressor.fit(X, Y)  # perform linear regression\n",
+    "Y_pred = linear_regressor.predict(X)  # make predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(linear_regressor.coef_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('ggplot')\n",
+    "plt.rcParams['axes.facecolor']='w'\n",
+    "plt.rcParams['axes.edgecolor']='555555'\n",
+    "#plt.rcParams['ytick.color']='black'\n",
+    "plt.rcParams['grid.color']='dddddd'\n",
+    "plt.rcParams['axes.spines.top']='false'\n",
+    "plt.rcParams['axes.spines.right']='false'\n",
+    "plt.rcParams['legend.frameon']='true'\n",
+    "plt.rcParams['legend.framealpha']='1'\n",
+    "plt.rcParams['legend.edgecolor']='1'\n",
+    "plt.rcParams['legend.borderpad']='1'\n",
+    "\n",
+    "\n",
+    "#filename = f\"exp{exp_id}_{benchmark}_{dim_value}_{instances}\"\n",
+    "\n",
+    "\n",
+    "t_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 4].values\n",
+    "y_warmup = input.loc[input['sec_start'] <= warmup_sec].iloc[:, 3].values\n",
+    "\n",
+    "plt.figure()\n",
+    "#plt.figure(figsize=(4, 3))\n",
+    "\n",
+    "plt.plot(X, Y, c=\"#348ABD\", label=\"observed\")\n",
+    "#plt.plot(t_warmup, y_warmup)\n",
+    "\n",
+    "plt.plot(X, Y_pred, c=\"#E24A33\", label=\"trend\") # color='red')\n",
+    "\n",
+    "#348ABD, 7A68A6, A60628, 467821, CF4457, 188487, E24A33\n",
+    "\n",
+    "plt.gca().yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, pos: '%1.0fK' % (x * 1e-3)))\n",
+    "plt.ylabel('queued messages')\n",
+    "plt.xlabel('seconds since start')\n",
+    "plt.legend()\n",
+    "#ax.set_ylim(ymin=0)\n",
+    "#ax.set_xlim(xmin=0)\n",
+    "\n",
+    "plt.savefig(\"plot.pdf\", bbox_inches='tight')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python",
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "version": "3.7.0-final"
+  },
+  "orig_nbformat": 2,
+  "file_extension": ".py",
+  "mimetype": "text/x-python",
+  "name": "python",
+  "npconvert_exporter": "python",
+  "pygments_lexer": "ipython3",
+  "version": 3,
+  "kernelspec": {
+   "name": "python37064bitvenvvenv469ea2e0a7854dc7b367eee45386afee",
+   "display_name": "Python 3.7.0 64-bit ('.venv': venv)"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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