algo_prototype.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The algorithm that need to be implemented in a scalable service\n",
    "\n",
    "from itertools import count\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def nd_rolling(data, window_size):\n",
    "    \"\"\"\n",
    "    data: pandas.core.frame.DataFrame\n",
    "    window_size: float\n",
    "\n",
    "    yield: tuple\n",
    "    \"\"\"\n",
    "\n",
    "    sample = list(zip(count(), data.values[:, 0], data.values[:, 1]))\n",
    "    for idx in range(0, len(sample)):\n",
    "        idx0 = idx if idx - window_size < 0 else idx - window_size\n",
    "        window = [it for it in sample\n",
    "                  if it[0] >= idx0 or it[0] <= idx0 + window_size]\n",
    "        x = np.array([it[2] for it in window])\n",
    "\n",
    "        yield {'idx': sample[idx][1],\n",
    "               'value': sample[idx][2],\n",
    "               'window_mean': np.mean(x),\n",
    "               'window_std': np.std(x)}\n",
    "\n",
    "def get_anomalous_values(data, window_size=100):\n",
    "    \"\"\"\n",
    "    data : pandas.core.frame.DataFrame\n",
    "    window_size: int\n",
    "\n",
    "    return: list\n",
    "    \"\"\"\n",
    "\n",
    "    # calculate the moving window for each point, and report the anomaly if\n",
    "    # the distance of the idx-th point is greater than md times the mahalanobis\n",
    "    # distance\n",
    "    return [(p['idx'], p['value']) for p in nd_rolling(data, window_size)\n",
    "            if abs(p['value'] - p['window_mean']) > 5 * p['window_std']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18:27:26.345</td>\n",
       "      <td>2.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18:27:26.346</td>\n",
       "      <td>2.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18:27:26.347</td>\n",
       "      <td>2.344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18:27:26.348</td>\n",
       "      <td>2.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18:27:26.349</td>\n",
       "      <td>2.343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      timestamp  value\n",
       "0  18:27:26.345  2.335\n",
       "1  18:27:26.346  2.335\n",
       "2  18:27:26.347  2.344\n",
       "3  18:27:26.348  2.347\n",
       "4  18:27:26.349  2.343"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test now the procedure with the test dataset, \"x_y_dataset.csv\"\n",
    "data = pd.read_csv(\"./data/sample.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "import matplotlib.pyplot as plt\n",
    "data_plot = data.plot(color = 'b')\n",
    "data_plot.set_xlabel(\"index in the time series\")\n",
    "data_plot.set_ylabel(\"value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('18:27:26.352', 2.449), ('18:27:26.411', 2.5540000000000003)]\n"
     ]
    }
   ],
   "source": [
    "print(get_anomalous_values(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}