{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "!pip install -Uqq nixtla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "from nixtla.utils import in_colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "IN_COLAB = in_colab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    from nixtla.utils import colab_badge\n",
    "    from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add confidence levels\n",
    "\n",
    "Tweak the confidence level used for historical anomaly detection. By default, if a value falls outside the 99% confidence interval, it is labeled as an anomaly.\n",
    "\n",
    "Modify this with the `level` parameter, which accepts any value between 0 and 100, including decimals.\n",
    "\n",
    "Increasing the `level` results in fewer anomalies detected, while decreasing the `level` increases the number of anomalies detected.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/capabilities/anomaly-detection/04_confidence_levels.ipynb)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| echo: false\n",
    "if not IN_COLAB:\n",
    "    load_dotenv()\n",
    "    colab_badge('docs/capabilities/historical-anomaly-detection/04_confidence_levels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from nixtla import NixtlaClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nixtla_client = NixtlaClient(\n",
    "    # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
    "    api_key = 'my_api_key_provided_by_nixtla'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 👍 Use an Azure AI endpoint\n",
    "> \n",
    "> To use an Azure AI endpoint, set the `base_url` argument:\n",
    "> \n",
    "> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    nixtla_client = NixtlaClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Querying model metadata...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 236616 characters]",
      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data\n",
    "df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/peyton-manning.csv')\n",
    "\n",
    "# Anomaly detection using a 70% confidence interval\n",
    "anomalies_df = nixtla_client.detect_anomalies(\n",
    "    df, \n",
    "    freq='D',\n",
    "    level=70\n",
    ")\n",
    "\n",
    "# Plot anomalies\n",
    "nixtla_client.plot(df, anomalies_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 📘 Available models in Azure AI\n",
    ">\n",
    "> If you use an Azure AI endpoint, set `model=\"azureai\"`\n",
    ">\n",
    "> `nixtla_client.detect_anomalies(..., model=\"azureai\")`\n",
    ">\n",
    "> For the public API, two models are supported: `timegpt-1` and `timegpt-1-long-horizon`.\n",
    "> \n",
    "> By default, `timegpt-1` is used. See [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) for details on using `timegpt-1-long-horizon`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information, read our detailed tutorial on [anomaly detection](https://docs.nixtla.io/docs/tutorials/anomaly_detection)."
   ]
  }
 ],
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