Anomaly Finder API Example Private Preview (Latest Method).ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Project Anomaly Finder API Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This Jupyter notebook shows you how to get started with the Project Anomaly Finder API in Python, and how to visualize your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from __future__ import print_function\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Import library to display results\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure,output_notebook, show\n",
    "from bokeh.palettes import Blues4\n",
    "from bokeh.models import ColumnDataSource,Slider\n",
    "import datetime\n",
    "from bokeh.io import push_notebook\n",
    "from dateutil import parser\n",
    "from ipywidgets import interact, widgets, fixed\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect(endpoint, subscription_key, request_data):\n",
    "    headers = {'Content-Type': 'application/json', 'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "    response = requests.post(endpoint, data=json.dumps(request_data), headers=headers)\n",
    "    if response.status_code == 200:\n",
    "        return json.loads(response.content.decode(\"utf-8\"))\n",
    "    else:\n",
    "        print(response.status_code)\n",
    "        raise Exception(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_figure(result, sample_data, sensitivity):\n",
    "    columns = {'ExpectedValues': result['ExpectedValues'], 'IsAnomaly': result['IsAnomaly'], 'IsNegativeAnomaly': result['IsNegativeAnomaly'],\n",
    "              'IsPositiveAnomaly': result['IsPositiveAnomaly'], 'UpperMargins': result['UpperMargins'], 'LowerMargins': result['LowerMargins']\n",
    "              , 'Value': [x['Value'] for x in sample_data['Series']], 'Timestamp': [parser.parse(x['Timestamp']) for x in sample_data['Series']]}\n",
    "    response = pd.DataFrame(data=columns)\n",
    "    values = response['Value']\n",
    "    label = response['Timestamp']\n",
    "    anomalies = []\n",
    "    anomaly_labels = []\n",
    "    index = 0\n",
    "    anomaly_indexes = []\n",
    "    p = figure(x_axis_type='datetime', title=\"Anomaly Finder Result\", width=800, height=600)\n",
    "    for anom in response['IsAnomaly']:\n",
    "        if anom == True and (values[index] > response.iloc[index]['ExpectedValues'] + response.iloc[index]['UpperMargins'] or \n",
    "                         values[index] < response.iloc[index]['ExpectedValues'] - response.iloc[index]['LowerMargins']):\n",
    "            anomalies.append(values[index])\n",
    "            anomaly_labels.append(label[index])\n",
    "            anomaly_indexes.append(index)\n",
    "        index = index+1\n",
    "    upperband = response['ExpectedValues'] + response['UpperMargins']\n",
    "    lowerband = response['ExpectedValues'] -response['LowerMargins']\n",
    "    band_x = np.append(label, label[::-1])\n",
    "    band_y = np.append(lowerband, upperband[::-1])\n",
    "    boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend='Boundary')\n",
    "    p.line(label, values, legend='Value', color=\"#2222aa\", line_width=1)\n",
    "    p.line(label, response['ExpectedValues'], legend='ExpectedValue',  line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n",
    "    anom_source = ColumnDataSource(dict(x=anomaly_labels, y=anomalies))\n",
    "    anoms = p.circle('x', 'y', size=5, color='tomato', source=anom_source)\n",
    "    p.legend.border_line_width = 1\n",
    "    p.legend.background_fill_alpha  = 0.1\n",
    "    show(p, notebook_handle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detect latest anomaly of sample timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find anomalies in batch will detect all points with same model, this method can not adapt to series pattern's change well. Usually, for online monitoring scenario, user can choose to detect latest one with a limited window size series, in the belowing example, we simulte a streaming scenario, each time we detect the latest one with 29 points before. (since period of the series is 7)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables\n",
    "endpoint_latest = 'https://westus2.api.cognitive.microsoft.com/anomalyfinder/v2.0/timeseries/last/detect'\n",
    "subscription_key = '' #Here you have to paste your primary key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_anomaly(sensitivity):\n",
    "    sample_data = json.load(open('sample.json'))\n",
    "    points = sample_data['Series']\n",
    "    skip_point = 29\n",
    "    result = {'ExpectedValues': [None]*len(points), 'UpperMargins': [None]*len(points), \n",
    "              'LowerMargins': [None]*len(points), 'IsNegativeAnomaly': [False]*len(points), \n",
    "              'IsPositiveAnomaly':[False]*len(points), 'IsAnomaly': [False]*len(points)}\n",
    "    anom_count = 0\n",
    "    for i in range(skip_point, len(points)+1):\n",
    "        single_sample_data = {}\n",
    "        single_sample_data['Series'] = points[i-29:i]\n",
    "        single_sample_data['Granularity'] = 'daily'\n",
    "        single_sample_data['MaxAnomalyRatio'] = 0.25\n",
    "        single_sample_data['Sensitivity'] = sensitivity\n",
    "        single_point = detect(endpoint_latest, subscription_key, single_sample_data)\n",
    "        if single_point['IsAnomaly'] == True:\n",
    "            anom_count = anom_count + 1\n",
    "\n",
    "        result['ExpectedValues'][i-1] = single_point['ExpectedValue']\n",
    "        result['UpperMargins'][i-1] = single_point['UpperMargin']\n",
    "        result['LowerMargins'][i-1] = single_point['LowerMargin']\n",
    "        result['IsNegativeAnomaly'][i-1] = single_point['IsNegativeAnomaly']\n",
    "        result['IsPositiveAnomaly'][i-1] = single_point['IsPositiveAnomaly']\n",
    "        result['IsAnomaly'][i-1] = single_point['IsAnomaly']\n",
    "    \n",
    "    build_figure(result, sample_data, sensitivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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Application\",\"version\":\"0.13.0\"}};\n",
       "  var render_items = [{\"docid\":\"64ca0ccc-0b14-4e24-8ebe-a4bae929a82b\",\"notebook_comms_target\":\"06039651-b17d-485a-be4e-3240d9e23249\",\"roots\":{\"9df5c3f4-8a60-4d7e-8904-a2864cdd5a5d\":\"c6209269-b5dd-4210-a50a-8081ab0cf31b\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "9df5c3f4-8a60-4d7e-8904-a2864cdd5a5d"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 95 sensitvity\n",
    "detect_anomaly(95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"7aa33069-1908-4e83-93c3-fd7d278337f0\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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Application\",\"version\":\"0.13.0\"}};\n",
       "  var render_items = [{\"docid\":\"3a38ecd2-28c3-4394-bc01-5c46b99e6452\",\"notebook_comms_target\":\"9cf9ba39-e6ea-4f25-a884-3cbfe9186b18\",\"roots\":{\"99b8a2ed-d05a-4fc0-90be-2a37f7a793f7\":\"7aa33069-1908-4e83-93c3-fd7d278337f0\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "99b8a2ed-d05a-4fc0-90be-2a37f7a793f7"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 85 sensitvity\n",
    "detect_anomaly(85)"
   ]
  }
 ],
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