{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Household energy consumption forecast\n",
    "#### *- Leveraging Amazon Forecast API -*\n",
    "---\n",
    "\n",
    "Previously, we leveraged the Amazon Forecast console to predict household energy consumption. We are now going to leverage the high level APIs from this service to achieve the same: **this notebook takes approximately an hour to run.**\n",
    "\n",
    "The overall process for using Amazon Forecast is the following:\n",
    "\n",
    "1. Create a **Dataset Group**, this is the large box that isolates models and the data they are trained on from each other. You can see that as an independant forecasting \"project\".\n",
    "1. Create a **Dataset**: in Forecast there are 3 types of dataset, *Target Time Series*, *Related Time Series*, and *Item Metadata*. The *Target Time Series* is required, the others provide additional context that certain algorithms can leverage.\n",
    "1. **Import data**, this moves the information from S3 into a storage volume where the data can be used for training and validation. You can see this as the ingestion process into the Forecast dataset.\n",
    "1. **Train a model**, Forecast automates this process for you but you can also select particular algorithms, and you can provide your own hyper parameters or use Hyper Parameter Optimization (HPO) to determine the most performant values for your data.\n",
    "1. **Deploy a Predictor**, here you are deploying your model so you can use it to generate a forecast.\n",
    "1. **Query the Forecast**, given a request bounded by time for an item, return the forecast for it. Once you have this you can evaluate its performance or use it to guide your decisions about the future.\n",
    "\n",
    "In this notebook we will be walking through the first 3 steps outlined above. One additional task that will be done here is to trim part of our training and validation data so that we can measure the accuracy of a forecast against our predictions.\n",
    "\n",
    "## Table Of Contents\n",
    "* **Preparation:**\n",
    "  * Setup\n",
    "  * Data Preparation\n",
    "  * Creating the Dataset Group and Dataset\n",
    "* **Building a predictor:**\n",
    "  * Create a Predictor\n",
    "  * Deploy a Predictor\n",
    "  * Obtain a Forecast\n",
    "* **Evaluating your forecast:** now is the time to pull down the predictions from this Predictor, and compare them to the actual observed values:\n",
    "  * Obtaining a Prediction\n",
    "  * Plotting the Actual Results\n",
    "  * Plotting the Prediction\n",
    "  * Comparing the Prediction to Actual Results\n",
    "* **Cleanup:** after building completing the notebooks you may want to delete the following to prevent any unwanted charges:\n",
    "  * Forecasts\n",
    "  * Predictors\n",
    "  * Datasets\n",
    "  * Dataset Groups\n",
    "\n",
    "For more informations about Amazon Forecast APIs, please check the [documentation](https://docs.aws.amazon.com/forecast/latest/dg/what-is-forecast.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IAM role and authorizations\n",
    "For the purpose of this sample, you need to make sure this SageMaker instance has the following authorizations:\n",
    "* `IAMFullAccess`\n",
    "* `AmazonForecastFullAccess`\n",
    "* `AmazonS3FullAccess`\n",
    "* `AmazonSageMakerFullAccess`\n",
    "\n",
    "Browse to the IAM Console and check that the role your SageMaker instance runs with if configured like this:\n",
    "\n",
    "![Datasets](pictures/iam-policy.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **PassRole** policy visible above is configured as an inline policy:\n",
    "\n",
    "```json\n",
    "{\n",
    "    \"Version\": \"2012-10-17\",\n",
    "    \"Statement\": [\n",
    "        {\n",
    "            \"Action\": [\n",
    "                \"iam:PassRole\"\n",
    "            ],\n",
    "            \"Effect\": \"Allow\",\n",
    "            \"Resource\": \"*\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "```\n",
    "\n",
    "Last but not least, also make sure that the **trust relationships** of this same role includes at least the following:\n",
    "\n",
    "![Datasets](pictures/iam-trust-relationship.png)\n",
    "\n",
    "You can click on the **Edit trust relationship** button as seen on the picture above and fill in the following JSON document:\n",
    "```json\n",
    "{\n",
    "    \"Version\": \"2012-10-17\",\n",
    "    \"Statement\": [{\n",
    "        \"Effect\": \"Allow\",\n",
    "        \"Principal\": {\n",
    "            \"Service\": [\n",
    "                \"sagemaker.amazonaws.com\",\n",
    "                \"forecast.amazonaws.com\",\n",
    "                \"s3.amazonaws.com\"\n",
    "            ]\n",
    "        },\n",
    "        \"Action\": \"sts:AssumeRole\"\n",
    "    }]\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the standard Python libraries that are used in this lesson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import botocore.exceptions\n",
    "import dateutil.parser\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import sagemaker\n",
    "import sys\n",
    "import time\n",
    "\n",
    "from datetime import datetime\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('Solarize_Light2')\n",
    "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
    "colors = prop_cycle.by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next part of the setup process is to validate that your account can communicate with Amazon Forecast, the cell below does just that. We also configure the S3 bucket name and region name for this notebook:\n",
    "- If you don't have an S3 bucket, create it first on S3 or use the default bucket attached to this notebook\n",
    "- Although we have set the region to eu-west-1 as a default value below, you can choose any of the regions that the service is available in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BUCKET = sagemaker.Session().default_bucket()\n",
    "PREFIX = 'forecast-workshop'\n",
    "region = 'eu-west-1'\n",
    "\n",
    "session = boto3.Session(region_name=region) \n",
    "forecast = session.client(service_name='forecast') \n",
    "forecastquery = session.client(service_name='forecastquery')\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths to local data:\n",
    "DATA = 'data'\n",
    "PROCESSED_DATA = 'data/processed'\n",
    "\n",
    "os.makedirs(PROCESSED_DATA, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "In the Data ingestion & EDA notebook, we already stored the hourly consumption of our households in a CSV file, we will load it here and prepare an appropriate input for Forecast. To begin, use Pandas to read the CSV and to show a sample of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>energy</th>\n",
       "      <th>client_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>23.648649</td>\n",
       "      <td>client_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>client_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>144.817073</td>\n",
       "      <td>client_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>client_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>266.369048</td>\n",
       "      <td>client_5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         energy client_id\n",
       "timestamp                                \n",
       "2014-01-01 01:00:00   23.648649  client_1\n",
       "2014-01-01 01:00:00    0.000000  client_2\n",
       "2014-01-01 01:00:00  144.817073  client_3\n",
       "2014-01-01 01:00:00   75.000000  client_4\n",
       "2014-01-01 01:00:00  266.369048  client_5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly_df = pd.read_csv(os.path.join(DATA, 'electricityusagedata.csv'), header=None, parse_dates=[0])\n",
    "hourly_df.columns = ['timestamp', 'energy', 'client_id']\n",
    "hourly_df = hourly_df.set_index('timestamp')\n",
    "hourly_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice in the output above there are 3 columns of data:\n",
    "\n",
    "1. The **Timestamp** (timestamp)\n",
    "1. A **Value** (energy)\n",
    "1. An **Item** (client_id)\n",
    "\n",
    "These are the 3 key required pieces of information to generate a forecast with Amazon Forecast. More can be added but these 3 must always remain present. Note that we don't have to rename our columns, as the data will be pushed without headers nor indexes to Forecast. This is just done for convenience in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at one of the time series: feel free to change the `client_id` in the next cell to explore other inputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "client_df = hourly_df[hourly_df['client_id'] == 'client_1']\n",
    "daily_rolling_average = client_df['energy'].rolling(window=24).mean()\n",
    "\n",
    "fig = plt.figure(figsize=(24,6))\n",
    "plt.plot(client_df['energy'], alpha=0.8, label='Energy consumption')\n",
    "\n",
    "# Adding daily rolling average:\n",
    "plt.plot(daily_rolling_average.index, daily_rolling_average, alpha=0.5, color='white', linewidth=4)\n",
    "plt.plot(daily_rolling_average.index, daily_rolling_average, color=colors[5], linewidth=1.5, label='Rolling average')\n",
    "\n",
    "plt.title(r'Energy consumption for $\\bf{client\\_1}$', fontsize=16)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Energy consumption (kWh)')\n",
    "plt.legend(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-01-01 01:00:00\n",
      "2015-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(np.min(hourly_df.index))\n",
    "print(np.max(hourly_df.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset happens to span January 01, 2014 to January 01, 2015. We will use the following train test split:\n",
    "\n",
    "* Training start: 2014-01-01 01:00:00\n",
    "* Training end: 2014-09-30 23:00:00\n",
    "* Testing start: 2014-10-01 00:00:00\n",
    "* Testing end: 2015-01-01 00:00:00\n",
    "\n",
    "Let's store these two ranges in different CSV files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_start = pd.to_datetime('2014-01-01 01:00:00')\n",
    "training_end   = pd.to_datetime('2014-09-30 23:00:00')\n",
    "testing_start  = pd.to_datetime('2014-10-01 00:00:00')\n",
    "testing_end    = pd.to_datetime('2015-01-01 00:00:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_df = hourly_df[training_start:training_end].reset_index()\n",
    "testing_df = hourly_df[testing_start:testing_end].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>energy</th>\n",
       "      <th>client_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>23.648649</td>\n",
       "      <td>client_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>client_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>144.817073</td>\n",
       "      <td>client_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>client_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>266.369048</td>\n",
       "      <td>client_5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp      energy client_id\n",
       "0 2014-01-01 01:00:00   23.648649  client_1\n",
       "1 2014-01-01 01:00:00    0.000000  client_2\n",
       "2 2014-01-01 01:00:00  144.817073  client_3\n",
       "3 2014-01-01 01:00:00   75.000000  client_4\n",
       "4 2014-01-01 01:00:00  266.369048  client_5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's export them to CSV files and place them into our `data/processed` folder: Amazon Forecast expects your CSV file to not include any header or index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_df.to_csv(f\"{PROCESSED_DATA}/train.csv\", header=False, index=False)\n",
    "testing_df.to_csv(f\"{PROCESSED_DATA}/test.csv\", header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 4.0M\n",
      "-rw-rw-r-- 1 ec2-user ec2-user 1021K Mar 10 06:05 test.csv\n",
      "-rw-rw-r-- 1 ec2-user ec2-user  3.0M Mar 10 06:05 train.csv\n"
     ]
    }
   ],
   "source": [
    "!ls -lh $PROCESSED_DATA/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this time the data is ready to be sent to S3 where Forecast will use it later. The following cells will upload the data to S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "KEY = f'{PREFIX}/train.csv'\n",
    "boto3.Session().resource('s3').Bucket(BUCKET).Object(KEY).upload_file(f'{PROCESSED_DATA}/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Dataset Group and Dataset <a class=\"anchor\" id=\"dataset\"></a>\n",
    "\n",
    "In Amazon Forecast , a dataset is a collection of file(s) which contain data that is relevant for a forecasting task. A dataset must conform to a schema provided by Amazon Forecast. \n",
    "\n",
    "More details about `Domain` and dataset type can be found on the [documentation](https://docs.aws.amazon.com/forecast/latest/dg/howitworks-domains-ds-types.html) . For this example, we are using [CUSTOM](https://docs.aws.amazon.com/forecast/latest/dg/custom-domain.html) domain with 3 required attributes `timestamp`, `target_value` and `item_id`.\n",
    "\n",
    "It is important to also convey how Amazon Forecast can understand your time series information. The cell immediately below does that, the next one configures your variable names for the Project, DatasetGroup, and Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_FREQUENCY = \"H\" \n",
    "TIMESTAMP_FORMAT = \"yyyy-MM-dd hh:mm:ss\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "project = 'energy_consumption_forecast'\n",
    "datasetName = project + '_ds'\n",
    "datasetGroupName = project + '_dsg'\n",
    "s3DataPath = \"s3://\" + BUCKET + \"/\" + KEY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Dataset Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Dataset group name: energy_consumption_forecast_dsg\n",
      "- Dataset group ARN: arn:aws:forecast:eu-west-1:123031033346:dataset-group/energy_consumption_forecast_dsg\n",
      "CPU times: user 13.2 ms, sys: 86 µs, total: 13.3 ms\n",
      "Wall time: 71.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Let's try to create a dataset group:\n",
    "try:\n",
    "    create_dataset_group_response = forecast.create_dataset_group(\n",
    "        DatasetGroupName=datasetGroupName,\n",
    "        Domain=\"CUSTOM\",\n",
    "    )\n",
    "    datasetGroupArn = create_dataset_group_response['DatasetGroupArn']\n",
    "    \n",
    "except Exception as e:\n",
    "    error_code = e.response['Error']['Code']\n",
    "    \n",
    "    # If the dataset group already exists, we get its ARN:\n",
    "    if (error_code == 'ResourceAlreadyExistsException'):\n",
    "        print('A dataset group with this name already exists, you can use it to create and ingest new datasets')\n",
    "        \n",
    "        # List all the existing dataset groups:\n",
    "        forecast_dsg_list = forecast.list_dataset_groups()\n",
    "\n",
    "        # Loop through all the Forecast dataset groups:\n",
    "        for dsg in forecast_dsg_list['DatasetGroups']:\n",
    "            # Get the project name (the string after the first delimiter in the ARN)\n",
    "            dsg_name = dsg['DatasetGroupArn'].split('/')[1]\n",
    "\n",
    "            # Once we find it, we store the ARN and break out of the loop:\n",
    "            if (dsg_name == datasetGroupName):\n",
    "                datasetGroupArn = dsg['DatasetGroupArn']\n",
    "                break\n",
    "                \n",
    "    else:\n",
    "        raise\n",
    "        \n",
    "print(f'- Dataset group name: {datasetGroupName}')\n",
    "print(f'- Dataset group ARN: {datasetGroupArn}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we browse to the console, we will see a new DatasetGroup has been created:\n",
    "\n",
    "![Dataset Group](pictures/api-dataset-group.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the API to get the metadata associated to this object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DatasetGroupName': 'energy_consumption_forecast_dsg',\n",
       " 'DatasetGroupArn': 'arn:aws:forecast:eu-west-1:123031033346:dataset-group/energy_consumption_forecast_dsg',\n",
       " 'DatasetArns': [],\n",
       " 'Domain': 'CUSTOM',\n",
       " 'Status': 'ACTIVE',\n",
       " 'CreationTime': datetime.datetime(2021, 3, 10, 6, 11, 7, 162000, tzinfo=tzlocal()),\n",
       " 'LastModificationTime': datetime.datetime(2021, 3, 10, 6, 11, 7, 162000, tzinfo=tzlocal()),\n",
       " 'ResponseMetadata': {'RequestId': 'd6287760-41a6-44bb-b896-afec31d507c1',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'content-type': 'application/x-amz-json-1.1',\n",
       "   'date': 'Wed, 10 Mar 2021 06:11:22 GMT',\n",
       "   'x-amzn-requestid': 'd6287760-41a6-44bb-b896-afec31d507c1',\n",
       "   'content-length': '285',\n",
       "   'connection': 'keep-alive'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.describe_dataset_group(DatasetGroupArn=datasetGroupArn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now define the schema: valid values for AttributeType are `string`, `integer`, `float` or `timestamp`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "schema = {\n",
    "   \"Attributes\": [\n",
    "      { \"AttributeName\": \"timestamp\",    \"AttributeType\": \"timestamp\" },\n",
    "      { \"AttributeName\": \"target_value\", \"AttributeType\": \"float\"     },\n",
    "      { \"AttributeName\": \"item_id\",      \"AttributeType\": \"string\"    }\n",
    "   ]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we are using the **same order** than the columns of the training dataset uploaded earlier: here, our `target_value` is the `energy` field and the `item_id` is the `client_id` field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>energy</th>\n",
       "      <th>client_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>23.648649</td>\n",
       "      <td>client_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>client_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>144.817073</td>\n",
       "      <td>client_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>client_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01 01:00:00</td>\n",
       "      <td>266.369048</td>\n",
       "      <td>client_5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp      energy client_id\n",
       "0 2014-01-01 01:00:00   23.648649  client_1\n",
       "1 2014-01-01 01:00:00    0.000000  client_2\n",
       "2 2014-01-01 01:00:00  144.817073  client_3\n",
       "3 2014-01-01 01:00:00   75.000000  client_4\n",
       "4 2014-01-01 01:00:00  266.369048  client_5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Dataset name: energy_consumption_forecast_ds\n",
      "- Dataset ARN: arn:aws:forecast:eu-west-1:123031033346:dataset/energy_consumption_forecast_ds\n",
      "CPU times: user 4.73 ms, sys: 79 µs, total: 4.81 ms\n",
      "Wall time: 71.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "try:\n",
    "    response = forecast.create_dataset(\n",
    "        Domain='CUSTOM',\n",
    "        DatasetType='TARGET_TIME_SERIES',\n",
    "        DatasetName=datasetName,\n",
    "        DataFrequency=DATASET_FREQUENCY, \n",
    "        Schema=schema\n",
    "    )\n",
    "    datasetArn = response['DatasetArn']\n",
    "    \n",
    "except Exception as e:\n",
    "    error_code = e.response['Error']['Code']\n",
    "\n",
    "    # If the dataset group already exists, we get its ARN:\n",
    "    if (error_code == 'ResourceAlreadyExistsException'):\n",
    "        print('A dataset with this name already exists, you can use it to ingest new data into it:')\n",
    "        \n",
    "        # List all the existing datasets:\n",
    "        forecast_ds_list = forecast.list_datasets()\n",
    "\n",
    "        # Loop through all the Forecast datasets:\n",
    "        for ds in forecast_ds_list['Datasets']:\n",
    "            # Get the project name (the string after the first delimiter in the ARN)\n",
    "            ds_name = ds['DatasetArn'].split('/')[1]\n",
    "\n",
    "            # Once we find it, we store the ARN and break out of the loop:\n",
    "            if (ds_name == datasetName):\n",
    "                datasetArn = ds['DatasetArn']\n",
    "                break\n",
    "                \n",
    "    else:\n",
    "        raise\n",
    "        \n",
    "print(f'- Dataset name: {datasetName}')\n",
    "print(f'- Dataset ARN: {datasetArn}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the API to get the metadata associated to this object: we can confirm that this is a target time series and that its frequency is hourly which is consistent with the training set we uploaded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DatasetArn': 'arn:aws:forecast:eu-west-1:123031033346:dataset/energy_consumption_forecast_ds',\n",
       " 'DatasetName': 'energy_consumption_forecast_ds',\n",
       " 'Domain': 'CUSTOM',\n",
       " 'DatasetType': 'TARGET_TIME_SERIES',\n",
       " 'DataFrequency': 'H',\n",
       " 'Schema': {'Attributes': [{'AttributeName': 'timestamp',\n",
       "    'AttributeType': 'timestamp'},\n",
       "   {'AttributeName': 'target_value', 'AttributeType': 'float'},\n",
       "   {'AttributeName': 'item_id', 'AttributeType': 'string'}]},\n",
       " 'EncryptionConfig': {},\n",
       " 'Status': 'ACTIVE',\n",
       " 'CreationTime': datetime.datetime(2021, 3, 10, 6, 11, 39, 670000, tzinfo=tzlocal()),\n",
       " 'LastModificationTime': datetime.datetime(2021, 3, 10, 6, 11, 39, 670000, tzinfo=tzlocal()),\n",
       " 'ResponseMetadata': {'RequestId': 'cd2b14a6-24f6-4bcc-b6c9-c2339aa51685',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'content-type': 'application/x-amz-json-1.1',\n",
       "   'date': 'Wed, 10 Mar 2021 06:12:08 GMT',\n",
       "   'x-amzn-requestid': 'cd2b14a6-24f6-4bcc-b6c9-c2339aa51685',\n",
       "   'content-length': '519',\n",
       "   'connection': 'keep-alive'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.describe_dataset(DatasetArn=datasetArn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Dataset to Dataset Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': 'ac8555e8-317f-4620-bcfc-eda5462f6dbf',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'content-type': 'application/x-amz-json-1.1',\n",
       "   'date': 'Wed, 10 Mar 2021 06:12:13 GMT',\n",
       "   'x-amzn-requestid': 'ac8555e8-317f-4620-bcfc-eda5462f6dbf',\n",
       "   'content-length': '2',\n",
       "   'connection': 'keep-alive'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.update_dataset_group(DatasetGroupArn=datasetGroupArn, DatasetArns=[datasetArn])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we browse to the console, we will see a new dataset is now attached to our dataset group. Its status is **active** but the latest import status mentions **Not uploaded**:\n",
    "\n",
    "![Datasets](pictures/api-datasets.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### Create IAM Role for Forecast\n",
    "\n",
    "Like many AWS services, Forecast will need to assume an IAM role in order to interact with your S3 resources securely. In this sample notebooks, we use the `get_or_create_iam_role()` utility function to create an IAM role:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_or_create_iam_role(role_name):\n",
    "    iam = boto3.client(\"iam\")\n",
    "\n",
    "    assume_role_policy_document = {\n",
    "        \"Version\": \"2012-10-17\",\n",
    "        \"Statement\": [\n",
    "            {\n",
    "              \"Effect\": \"Allow\",\n",
    "              \"Principal\": {\n",
    "                \"Service\": \"forecast.amazonaws.com\"\n",
    "              },\n",
    "              \"Action\": \"sts:AssumeRole\"\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "\n",
    "    try:\n",
    "        create_role_response = iam.create_role(\n",
    "            RoleName = role_name,\n",
    "            AssumeRolePolicyDocument = json.dumps(assume_role_policy_document)\n",
    "        )\n",
    "        role_arn = create_role_response[\"Role\"][\"Arn\"]\n",
    "        print(\"Created\", role_arn)\n",
    "        \n",
    "    except iam.exceptions.EntityAlreadyExistsException:\n",
    "        print(\"The role \" + role_name + \" exists, ignore to create it\")\n",
    "        role_arn = boto3.resource('iam').Role(role_name).arn\n",
    "\n",
    "    print(\"Attaching policies\")\n",
    "\n",
    "    iam.attach_role_policy(\n",
    "        RoleName = role_name,\n",
    "        PolicyArn = \"arn:aws:iam::aws:policy/AmazonForecastFullAccess\"\n",
    "    )\n",
    "\n",
    "    iam.attach_role_policy(\n",
    "        RoleName=role_name,\n",
    "        PolicyArn='arn:aws:iam::aws:policy/AmazonS3FullAccess',\n",
    "    )\n",
    "\n",
    "    print(\"Waiting for a minute to allow IAM role policy attachment to propagate\")\n",
    "    time.sleep(60)\n",
    "\n",
    "    print(\"Done.\")\n",
    "    return role_arn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created arn:aws:iam::123031033346:role/ForecastNotebookRole-EnergyConsumptionPrediction\n",
      "Attaching policies\n",
      "Waiting for a minute to allow IAM role policy attachment to propagate\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# Create the role to provide to Amazon Forecast.\n",
    "role_name = \"ForecastNotebookRole-EnergyConsumptionPrediction\"\n",
    "role_arn = get_or_create_iam_role(role_name=role_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Data Import Job\n",
    "\n",
    "\n",
    "Now that Forecast knows how to understand the CSV we are providing, the next step is to import the data from S3 into Amazon Forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasetImportJobName = 'energy_consumption_dataset_import_job'\n",
    "ds_import_job_response = forecast.create_dataset_import_job(\n",
    "    DatasetImportJobName=datasetImportJobName,\n",
    "    DatasetArn=datasetArn,\n",
    "    DataSource= {\n",
    "        \"S3Config\" : {\n",
    "            \"Path\": s3DataPath,\n",
    "            \"RoleArn\": role_arn\n",
    "        }\n",
    "    },\n",
    "    TimestampFormat=TIMESTAMP_FORMAT\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arn:aws:forecast:eu-west-1:123031033346:dataset-import-job/energy_consumption_forecast_ds/energy_consumption_dataset_import_job\n"
     ]
    }
   ],
   "source": [
    "ds_import_job_arn=ds_import_job_response['DatasetImportJobArn']\n",
    "print(ds_import_job_arn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the status of dataset, when the status change from **CREATE_IN_PROGRESS** to **ACTIVE**, we can continue to next steps. Depending on the data size. It can take 10 mins to be **ACTIVE**. This process will take 5 to 10 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE_PENDING\n",
      "CREATE_IN_PROGRESS\n",
      "CREATE_IN_PROGRESS\n",
      "CREATE_IN_PROGRESS\n",
      "CREATE_IN_PROGRESS\n",
      "CREATE_IN_PROGRESS\n",
      "CREATE_IN_PROGRESS\n",
      "CREATE_IN_PROGRESS\n",
      "CREATE_IN_PROGRESS\n",
      "CREATE_IN_PROGRESS\n",
      "ACTIVE\n",
      "ACTIVE\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    status = forecast.describe_dataset_import_job(DatasetImportJobArn=ds_import_job_arn)['Status']\n",
    "    print(str(pd.to_datetime(datetime.now()))[:19], \"| Data ingestion:\", status)\n",
    "    \n",
    "    if status in ('ACTIVE', 'CREATE_FAILED'): break\n",
    "    time.sleep(60)\n",
    "\n",
    "print(status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DatasetImportJobName': 'energy_consumption_dataset_import_job',\n",
       " 'DatasetImportJobArn': 'arn:aws:forecast:eu-west-1:123031033346:dataset-import-job/energy_consumption_forecast_ds/energy_consumption_dataset_import_job',\n",
       " 'DatasetArn': 'arn:aws:forecast:eu-west-1:123031033346:dataset/energy_consumption_forecast_ds',\n",
       " 'TimestampFormat': 'yyyy-MM-dd hh:mm:ss',\n",
       " 'UseGeolocationForTimeZone': False,\n",
       " 'DataSource': {'S3Config': {'Path': 's3://sagemaker-eu-west-1-123031033346/forecast-workshop/train.csv',\n",
       "   'RoleArn': 'arn:aws:iam::123031033346:role/ForecastNotebookRole-EnergyConsumptionPrediction'}},\n",
       " 'FieldStatistics': {'item_id': {'Count': 65510,\n",
       "   'CountDistinct': 10,\n",
       "   'CountNull': 0},\n",
       "  'target_value': {'Count': 65510,\n",
       "   'CountDistinct': 8876,\n",
       "   'CountNull': 0,\n",
       "   'CountNan': 0,\n",
       "   'Min': '0.0',\n",
       "   'Max': '471.3804713804715',\n",
       "   'Avg': 77.55309140070656,\n",
       "   'Stddev': 82.24114755464895},\n",
       "  'timestamp': {'Count': 65510,\n",
       "   'CountDistinct': 6551,\n",
       "   'CountNull': 0,\n",
       "   'Min': '2014-01-01T01:00:00Z',\n",
       "   'Max': '2014-09-30T23:00:00Z'}},\n",
       " 'DataSize': 0.002887023612856865,\n",
       " 'Status': 'ACTIVE',\n",
       " 'CreationTime': datetime.datetime(2021, 3, 10, 6, 14, 24, 853000, tzinfo=tzlocal()),\n",
       " 'LastModificationTime': datetime.datetime(2021, 3, 10, 6, 24, 24, 335000, tzinfo=tzlocal()),\n",
       " 'ResponseMetadata': {'RequestId': 'cb09a91f-5f25-476e-9f47-ffba0252c5b8',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'content-type': 'application/x-amz-json-1.1',\n",
       "   'date': 'Wed, 10 Mar 2021 06:24:39 GMT',\n",
       "   'x-amzn-requestid': 'cb09a91f-5f25-476e-9f47-ffba0252c5b8',\n",
       "   'content-length': '1267',\n",
       "   'connection': 'keep-alive'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.describe_dataset_import_job(DatasetImportJobArn=ds_import_job_arn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point you have successfully imported your data into Amazon Forecast and now it is time to get started to build your first model. The import status now mentions **Active**:\n",
    "\n",
    "![Datasets](pictures/api-datasets-active.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a predictor\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Predictor\n",
    "\n",
    "Your data was imported to be used by Amazon Forecast, here we will once again define our dataset information and then start building a model (or **predictor** in Amazon Forecast own words)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Forecast horizon` is the number of time points to predict in the future. For weekly data, a value of 12 means 12 weeks. Our example is hourly data, we would like to forecast what happens the next day, so we can set this value to 24 (meaning 24 hours).\n",
    "\n",
    "Every Amazon Forecast predictor uses an **algorithm** to train a model, then uses the **trained model** to make a **forecast** using an input dataset group. To help you get started, Amazon Forecast provides the following predefined algorithms:\n",
    "\n",
    "* CNN-QR: `arn:aws:forecast:::algorithm/CNN-QR`\n",
    "* DeepAR+: `arn:aws:forecast:::algorithm/Deep_AR_Plus`\n",
    "* Prophet: `arn:aws:forecast:::algorithm/Prophet`\n",
    "* Exponential Smoothing (ETS): `arn:aws:forecast:::algorithm/ETS`\n",
    "* Autoregressive Integrated Moving Average (ARIMA): `arn:aws:forecast:::algorithm/ARIMA`\n",
    "* Non-Parametric Time Series (NPTS): `arn:aws:forecast:::algorithm/NPTS`\n",
    "\n",
    "For more details about the different algorithms and how to choose them, check the [documentation](https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-choosing-recipes.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Amazon Forecast DeepAR+** is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNNs). Classical forecasting methods, such as *autoregressive integrated moving average* (ARIMA) or *exponential smoothing* (ETS), fit a single model to each individual time series, and then use that model to extrapolate the time series into the future.\n",
    "\n",
    "In many applications, however, you have many similar time series across a set of cross-sectional units. These time-series can relate to different clients, products, server loads, or requests for web pages. In this case, it can be beneficial to train a single model jointly over all of the time series. DeepAR+ takes this approach. **When your dataset contains hundreds of feature time series, the DeepAR+ algorithm outperforms the standard ARIMA and ETS methods**. You can also use the trained model for generating forecasts for new time series that are similar to the ones it has been trained on (**cold start** for never-seen before signals).\n",
    "\n",
    "In the case of our energy dataset, we only have 10 different households:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(training_df['client_id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given this small number of time series, we will select ARIMA, which we will do in the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictorName= project + '_arima'\n",
    "forecastHorizon = 24\n",
    "algorithmArn = 'arn:aws:forecast:::algorithm/ARIMA'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create our **Predictor** by training a model on the data provided to Amazon Forecast. In the background, Amazon Forecast cut the most recent data to build a train / validation split. The model is trained on the new training split and the performance metrics are evaluated against the new validation split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Predictor name: energy_consumption_forecast_arima\n",
      "- Predictor ARN: arn:aws:forecast:eu-west-1:123031033346:predictor/energy_consumption_forecast_arima\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    create_predictor_response = forecast.create_predictor(\n",
    "        PredictorName = predictorName, \n",
    "        AlgorithmArn = algorithmArn,\n",
    "        ForecastHorizon = forecastHorizon,\n",
    "        PerformAutoML = False,\n",
    "        PerformHPO = False,\n",
    "        EvaluationParameters= {\n",
    "            \"NumberOfBacktestWindows\": 1, \n",
    "            \"BackTestWindowOffset\": 24\n",
    "        }, \n",
    "        InputDataConfig = {\"DatasetGroupArn\": datasetGroupArn},\n",
    "        FeaturizationConfig = {\n",
    "            \"ForecastFrequency\": \"H\", \n",
    "            \"Featurizations\": [{\n",
    "                \"AttributeName\": \"target_value\", \n",
    "                \"FeaturizationPipeline\": [{\n",
    "                    \"FeaturizationMethodName\": \"filling\", \n",
    "                    \"FeaturizationMethodParameters\": {\n",
    "                        \"frontfill\": \"none\", \n",
    "                        \"middlefill\": \"zero\", \n",
    "                        \"backfill\": \"zero\"\n",
    "                    }\n",
    "                }]\n",
    "            }]\n",
    "        }\n",
    "    )\n",
    "\n",
    "    predictor_arn = create_predictor_response['PredictorArn']\n",
    "    \n",
    "except Exception as e:\n",
    "    error_code = e.response['Error']['Code']\n",
    "\n",
    "    # If the predictor already exists, we get its ARN:\n",
    "    if (error_code == 'ResourceAlreadyExistsException'):\n",
    "        print('A predictor with this name already exists, you can query it to check its status or request a forecast:')\n",
    "        \n",
    "        # List all the existing predictors:\n",
    "        forecast_pred_list = forecast.list_predictors()\n",
    "\n",
    "        # Loop through all the Forecast predictors:\n",
    "        for predictor in forecast_pred_list['Predictors']:\n",
    "            # Get the project name (the string after the first delimiter in the ARN)\n",
    "            predictor_name = predictor['PredictorArn'].split('/')[1]\n",
    "\n",
    "            # Once we find it, we store the ARN and break out of the loop:\n",
    "            if (predictor_name == predictorName):\n",
    "                predictor_arn = predictor['PredictorArn']\n",
    "                break\n",
    "                   \n",
    "    else:\n",
    "        raise\n",
    "        \n",
    "print(f'- Predictor name: {predictorName}')\n",
    "print(f'- Predictor ARN: {predictor_arn}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our predictor (model) is now training as we can see in the console:\n",
    "\n",
    "![Datasets](pictures/api-predictor-training.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the status of the predictor: when the status change from **CREATE_IN_PROGRESS** to **ACTIVE**, we can continue to next steps. Depending on data size, model selection and hyper parameters，it can take 10 mins to more than one hour to be **ACTIVE**. This is  still a training task so for this model, it will take around 15 minutes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-10 06:27:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:28:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:29:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:30:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:31:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:32:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:33:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:34:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:35:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:36:40 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:37:41 | Model training: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:38:41 | Model training: ACTIVE\n",
      "ACTIVE\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    status = forecast.describe_predictor(PredictorArn=predictor_arn)['Status']\n",
    "    print(str(pd.to_datetime(datetime.now()))[:19], \"| Model training:\", status)\n",
    "    \n",
    "    if status in ('ACTIVE', 'CREATE_FAILED'): break\n",
    "    time.sleep(60)\n",
    "\n",
    "print(status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Error Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our predictor is now **active** and we can see in the console the associated performance (WAPE and RMSE metric computed on the validation dataset). You can click on the predictor name to see more details or to download the backtest results for instance:\n",
    "\n",
    "![Datasets](pictures/api-predictor-active.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also collect these metrics through the API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PredictorEvaluationResults': [{'AlgorithmArn': 'arn:aws:forecast:::algorithm/ARIMA',\n",
       "   'TestWindows': [{'EvaluationType': 'SUMMARY',\n",
       "     'Metrics': {'RMSE': 13.508326497982193,\n",
       "      'WeightedQuantileLosses': [{'Quantile': 0.9,\n",
       "        'LossValue': 0.04936248161035917},\n",
       "       {'Quantile': 0.5, 'LossValue': 0.10099251983836498},\n",
       "       {'Quantile': 0.1, 'LossValue': 0.05279812945536947}],\n",
       "      'ErrorMetrics': [{'ForecastType': 'mean',\n",
       "        'WAPE': 0.10099251983836498,\n",
       "        'RMSE': 13.508326497982193}]}},\n",
       "    {'TestWindowStart': datetime.datetime(2014, 9, 30, 0, 0, tzinfo=tzlocal()),\n",
       "     'TestWindowEnd': datetime.datetime(2014, 10, 1, 0, 0, tzinfo=tzlocal()),\n",
       "     'ItemCount': 10,\n",
       "     'EvaluationType': 'COMPUTED',\n",
       "     'Metrics': {'RMSE': 13.508326497982193,\n",
       "      'WeightedQuantileLosses': [{'Quantile': 0.9,\n",
       "        'LossValue': 0.04936248161035918},\n",
       "       {'Quantile': 0.5, 'LossValue': 0.10099251983836498},\n",
       "       {'Quantile': 0.1, 'LossValue': 0.05279812945536947}],\n",
       "      'ErrorMetrics': [{'ForecastType': 'mean',\n",
       "        'WAPE': 0.10099251983836498,\n",
       "        'RMSE': 13.508326497982193}]}}]}],\n",
       " 'ResponseMetadata': {'RequestId': '86796595-f493-4247-a529-5e0b46022d4c',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'content-type': 'application/x-amz-json-1.1',\n",
       "   'date': 'Wed, 10 Mar 2021 06:38:40 GMT',\n",
       "   'x-amzn-requestid': '86796595-f493-4247-a529-5e0b46022d4c',\n",
       "   'content-length': '846',\n",
       "   'connection': 'keep-alive'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.get_accuracy_metrics(PredictorArn=predictor_arn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Forecast\n",
    "\n",
    "Now we can create a forecast using the predictor that was trained. During the **first forecast creation**, Amazon Forecast **retrains** a model on the entire dataset: this time, it doesn't split the training set in train / validation, it uses your whole training data to train a model for every item_id.\n",
    "\n",
    "Once the new model is trained, it is hosted (similarly to a SageMaker Endpoint, it provisions the necessary resources to host the trained model so that it can serve predictions) so that it can perform inference. This `CreateForecast` operation creates a forecast for every item (item_id) in the dataset group that was used to train the predictor. After a forecast is created, you can query the forecast or export it to an Amazon S3 bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecastName = project + '_arima_forecast'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Forecast name: energy_consumption_forecast_arima_forecast\n",
      "- Forecast ARN: arn:aws:forecast:eu-west-1:123031033346:forecast/energy_consumption_forecast_arima_forecast\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    create_forecast_response = forecast.create_forecast(\n",
    "        ForecastName=forecastName,\n",
    "        PredictorArn=predictor_arn\n",
    "    )\n",
    "    forecast_arn = create_forecast_response['ForecastArn']\n",
    "    \n",
    "except Exception as e:\n",
    "    error_code = e.response['Error']['Code']\n",
    "\n",
    "    # If the predictor already exists, we get its ARN:\n",
    "    if (error_code == 'ResourceAlreadyExistsException'):\n",
    "        print('A forecast with this name already exists, you can use it to obtain a prediction:')\n",
    "        \n",
    "        # List all the existing predictors:\n",
    "        forecast_fc_list = forecast.list_forecasts()\n",
    "\n",
    "        # Loop through all the Forecast forecasts:\n",
    "        for fc in forecast_fc_list['Forecasts']:\n",
    "            # Get the project name (the string after the first delimiter in the ARN)\n",
    "            fc_name = fc['ForecastArn'].split('/')[1]\n",
    "\n",
    "            # Once we find it, we store the ARN and break out of the loop:\n",
    "            if (fc_name == forecastName):\n",
    "                forecast_arn = fc['ForecastArn']\n",
    "                break\n",
    "                \n",
    "    else:\n",
    "        raise\n",
    "        \n",
    "print(f'- Forecast name: {forecastName}')\n",
    "print(f'- Forecast ARN: {forecast_arn}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our forecast is now provisioning as we can see in the console:\n",
    "\n",
    "![Datasets](pictures/api-forecast-provisioning.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the status of the forecast: when the status change from **CREATE_IN_PROGRESS** to **ACTIVE**, we can continue to next steps. Depending on model size (that depends on algorithm selection) and number of items，it can take 10 mins to more than one hour to be **ACTIVE**. For this model, it will take 15-20 minutes for the **first** forecast:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-03-10 06:38:41 | Forecast generation: CREATE_PENDING\n",
      "2021-03-10 06:39:41 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:40:41 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:41:41 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:42:41 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:43:41 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:44:41 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:45:42 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:46:42 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:47:42 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:48:42 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:49:42 | Forecast generation: CREATE_IN_PROGRESS\n",
      "2021-03-10 06:50:42 | Forecast generation: ACTIVE\n",
      "ACTIVE\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    status = forecast.describe_forecast(ForecastArn=forecast_arn)['Status']\n",
    "    print(str(pd.to_datetime(datetime.now()))[:19], \"| Forecast generation:\", status)\n",
    "    \n",
    "    if status in ('ACTIVE', 'CREATE_FAILED'): break\n",
    "    time.sleep(60)\n",
    "\n",
    "print(status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our forecast has finished training on the whole dataset and is now ready to be queried:\n",
    "\n",
    "![Datasets](pictures/api-forecast-active.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating your forecast\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get a forecast (obtaining a prediction)\n",
    "Once created, the forecast results are ready to be viewed. To query a forecast, you can specify dates (use the ISO 8601 format for this: `yyyy-MM-dd'T'HH:mm:ss`. For example, `2015-01-01T08:00:00`) or the `query_forecast` will send back the whole forecast horizon. Our forecast horizon is 24 hours, so that will be 24 data points for this example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arn:aws:forecast:eu-west-1:123031033346:forecast/energy_consumption_forecast_arima_forecast\n",
      "\n",
      "{'Forecast': {'Predictions': {'p10': [{'Timestamp': '2014-10-01T00:00:00', 'Value': 21.8402156829834}, {'Timestamp': '2014-10-01T01:00:00', 'Value': 18.716354370117188}, {'Timestamp': '2014-10-01T02:00:00', 'Value': 16.825145721435547}, {'Timestamp': '2014-10-01T03:00:00', 'Value': 14.990082740783691}, {'Timestamp': '2014-10-01T04:00:00', 'Value': 13.274307250976562}, {'Timestamp': '2014-10-01T05:00:00', 'Value': 14.874940872192383}, {'Timestamp': '2014-10-01T06:00:00', 'Value': 17.804637908935547}, {'Timestamp': '2014-10-01T07:00:00', 'Value': 21.146377563476562}, {'Timestamp': '2014-10-01T08:00:00', 'Value': 24.23131561279297}, {'Timestamp': '2014-10-01T09:00:00', 'Value': 24.694671630859375}, {'Timestamp': '2014-10-01T10:00:00', 'Value': 23.99750518798828}, {'Timestamp': '2014-10-01T11:00:00', 'Value': 23.321178436279297}, {'Timestamp': '2014-10-01T12:00:00', 'Value': 22.47322654724121}, {'Timestamp': '2014-10-01T13:00:00', 'Value': 22.18488883972168}, {'Timestamp': '2014-10-01T14:00:00', 'Value': 21.413389205932617}, {'Timestamp': '2014-10-01T15:00:00', 'Value': 19.854827880859375}, {'Timestamp': '2014-10-01T16:00:00', 'Value': 18.956344604492188}, {'Timestamp': '2014-10-01T17:00:00', 'Value': 19.13568687438965}, {'Timestamp': '2014-10-01T18:00:00', 'Value': 20.702733993530273}, {'Timestamp': '2014-10-01T19:00:00', 'Value': 22.98016357421875}, {'Timestamp': '2014-10-01T20:00:00', 'Value': 24.125774383544922}, {'Timestamp': '2014-10-01T21:00:00', 'Value': 23.680652618408203}, {'Timestamp': '2014-10-01T22:00:00', 'Value': 21.997127532958984}, {'Timestamp': '2014-10-01T23:00:00', 'Value': 19.743024826049805}], 'p50': [{'Timestamp': '2014-10-01T00:00:00', 'Value': 24.470705032348633}, {'Timestamp': '2014-10-01T01:00:00', 'Value': 22.22030258178711}, {'Timestamp': '2014-10-01T02:00:00', 'Value': 20.623361587524414}, {'Timestamp': '2014-10-01T03:00:00', 'Value': 18.916597366333008}, {'Timestamp': '2014-10-01T04:00:00', 'Value': 17.374601364135742}, {'Timestamp': '2014-10-01T05:00:00', 'Value': 19.188426971435547}, {'Timestamp': '2014-10-01T06:00:00', 'Value': 22.23379898071289}, {'Timestamp': '2014-10-01T07:00:00', 'Value': 25.75410270690918}, {'Timestamp': '2014-10-01T08:00:00', 'Value': 28.99951934814453}, {'Timestamp': '2014-10-01T09:00:00', 'Value': 29.580671310424805}, {'Timestamp': '2014-10-01T10:00:00', 'Value': 29.05069351196289}, {'Timestamp': '2014-10-01T11:00:00', 'Value': 28.504926681518555}, {'Timestamp': '2014-10-01T12:00:00', 'Value': 27.779582977294922}, {'Timestamp': '2014-10-01T13:00:00', 'Value': 27.640199661254883}, {'Timestamp': '2014-10-01T14:00:00', 'Value': 26.98429298400879}, {'Timestamp': '2014-10-01T15:00:00', 'Value': 25.550212860107422}, {'Timestamp': '2014-10-01T16:00:00', 'Value': 24.782821655273438}, {'Timestamp': '2014-10-01T17:00:00', 'Value': 25.071182250976562}, {'Timestamp': '2014-10-01T18:00:00', 'Value': 26.760282516479492}, {'Timestamp': '2014-10-01T19:00:00', 'Value': 29.154695510864258}, {'Timestamp': '2014-10-01T20:00:00', 'Value': 30.406545639038086}, {'Timestamp': '2014-10-01T21:00:00', 'Value': 30.077869415283203}, {'Timestamp': '2014-10-01T22:00:00', 'Value': 28.501510620117188}, {'Timestamp': '2014-10-01T23:00:00', 'Value': 26.35181427001953}], 'p90': [{'Timestamp': '2014-10-01T00:00:00', 'Value': 27.101192474365234}, {'Timestamp': '2014-10-01T01:00:00', 'Value': 25.72425079345703}, {'Timestamp': '2014-10-01T02:00:00', 'Value': 24.42157745361328}, {'Timestamp': '2014-10-01T03:00:00', 'Value': 22.843111038208008}, {'Timestamp': '2014-10-01T04:00:00', 'Value': 21.474895477294922}, {'Timestamp': '2014-10-01T05:00:00', 'Value': 23.50191307067871}, {'Timestamp': '2014-10-01T06:00:00', 'Value': 26.662960052490234}, {'Timestamp': '2014-10-01T07:00:00', 'Value': 30.361827850341797}, {'Timestamp': '2014-10-01T08:00:00', 'Value': 33.767723083496094}, {'Timestamp': '2014-10-01T09:00:00', 'Value': 34.466670989990234}, {'Timestamp': '2014-10-01T10:00:00', 'Value': 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{'content-type': 'application/x-amz-json-1.1', 'date': 'Wed, 10 Mar 2021 06:50:41 GMT', 'x-amzn-requestid': '82b59d62-3bb7-4925-b911-0317e47d9698', 'content-length': '4561', 'connection': 'keep-alive'}, 'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "client_id = 'client_1'\n",
    "\n",
    "print(forecast_arn)\n",
    "print()\n",
    "forecastResponse = forecastquery.query_forecast(\n",
    "    ForecastArn=forecast_arn,\n",
    "    Filters={'item_id': client_id}\n",
    ")\n",
    "print(forecastResponse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the actual results\n",
    "\n",
    "Earlier we created a file of observed values for both the training and the testing periods, we are now going to select a given date and client_id from that dataframe and are going to plot the actual consumption data for that customer. We need to reduce the data to just the day we wish to plot, which is October, 1st 2014, and we only grab the items for household `client_1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>actual</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01 19:00:00</th>\n",
       "      <td>31.650071</td>\n",
       "      <td>client_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 20:00:00</th>\n",
       "      <td>36.273115</td>\n",
       "      <td>client_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 21:00:00</th>\n",
       "      <td>32.894737</td>\n",
       "      <td>client_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 22:00:00</th>\n",
       "      <td>28.982930</td>\n",
       "      <td>client_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 23:00:00</th>\n",
       "      <td>28.093883</td>\n",
       "      <td>client_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        actual   item_id\n",
       "timestamp                               \n",
       "2014-10-01 19:00:00  31.650071  client_1\n",
       "2014-10-01 20:00:00  36.273115  client_1\n",
       "2014-10-01 21:00:00  32.894737  client_1\n",
       "2014-10-01 22:00:00  28.982930  client_1\n",
       "2014-10-01 23:00:00  28.093883  client_1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_train_df = pd.read_csv(f'{PROCESSED_DATA}/train.csv', names=['timestamp','actual','item_id'], parse_dates=[0])\n",
    "actual_train_df = actual_train_df[(actual_train_df['item_id'] == client_id)]\n",
    "\n",
    "testing_start  = pd.to_datetime('2014-10-01 00:00:00')\n",
    "testing_end    = pd.to_datetime('2014-10-01 23:00:00')\n",
    "actual_test_df = pd.read_csv(f'{PROCESSED_DATA}/test.csv', names=['timestamp','actual','item_id'], parse_dates=[0])\n",
    "actual_train_df = actual_train_df.append(actual_test_df.iloc[0], ignore_index=True)\n",
    "actual_train_df = actual_train_df.set_index('timestamp')\n",
    "actual_test_df = actual_test_df.set_index('timestamp')\n",
    "actual_test_df = actual_test_df[testing_start:testing_end]\n",
    "actual_test_df = actual_test_df[(actual_test_df['item_id'] == client_id)]\n",
    "\n",
    "actual_test_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post-processing the prediction\n",
    "\n",
    "Next we need to convert the JSON response from the Predictor to a dataframe that we can plot. The cell below generates a dataframe for p10, p50 and p90. `p50` gives the prediction median while `p90 - p10` gives the 80% confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_df = dict()\n",
    "for percentile in ['p10', 'p50', 'p90']:\n",
    "    predictions_df.update({percentile: pd.DataFrame.from_dict(forecastResponse['Forecast']['Predictions'][percentile])})\n",
    "    predictions_df[percentile]['Timestamp'] = pd.to_datetime(predictions_df[percentile]['Timestamp'])\n",
    "    predictions_df[percentile].columns = ['Timestamp', percentile]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>actual</th>\n",
       "      <th>p10</th>\n",
       "      <th>p50</th>\n",
       "      <th>p90</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01 00:00:00</th>\n",
       "      <td>24.537696</td>\n",
       "      <td>21.840216</td>\n",
       "      <td>24.470705</td>\n",
       "      <td>27.101192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 01:00:00</th>\n",
       "      <td>22.226174</td>\n",
       "      <td>18.716354</td>\n",
       "      <td>22.220303</td>\n",
       "      <td>25.724251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 02:00:00</th>\n",
       "      <td>19.381223</td>\n",
       "      <td>16.825146</td>\n",
       "      <td>20.623362</td>\n",
       "      <td>24.421577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 03:00:00</th>\n",
       "      <td>19.203414</td>\n",
       "      <td>14.990083</td>\n",
       "      <td>18.916597</td>\n",
       "      <td>22.843111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01 04:00:00</th>\n",
       "      <td>19.025605</td>\n",
       "      <td>13.274307</td>\n",
       "      <td>17.374601</td>\n",
       "      <td>21.474895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        actual        p10        p50        p90\n",
       "timestamp                                                      \n",
       "2014-10-01 00:00:00  24.537696  21.840216  24.470705  27.101192\n",
       "2014-10-01 01:00:00  22.226174  18.716354  22.220303  25.724251\n",
       "2014-10-01 02:00:00  19.381223  16.825146  20.623362  24.421577\n",
       "2014-10-01 03:00:00  19.203414  14.990083  18.916597  22.843111\n",
       "2014-10-01 04:00:00  19.025605  13.274307  17.374601  21.474895"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.merge(actual_test_df.reset_index(), predictions_df['p10'], how='inner', left_on=['timestamp'], right_on='Timestamp')\n",
    "\n",
    "for percentile in ['p50', 'p90']:\n",
    "    results_df = pd.merge(results_df, predictions_df[percentile], how='inner', left_on=['timestamp'], right_on='Timestamp')\n",
    "\n",
    "results_df = results_df[['timestamp', 'actual', 'p10', 'p50', 'p90']]\n",
    "results_df = results_df.set_index('timestamp')\n",
    "results_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the prediction to actual results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, plot the actual data during the evaluation period:\n",
    "fig = plt.figure(figsize=(24,6))\n",
    "plt.plot(results_df['actual'], label='Actual data', linewidth=1, color='#AAAAAA', linestyle='--', alpha=0.8)\n",
    "# plt.plot(results_df['actual'], linewidth=5, color=colors[4], alpha=0.1)\n",
    "\n",
    "# Next, plot the prediction enveloppe and the median value predicted:\n",
    "plt.plot(results_df['p50'], label='Prediction median', color=colors[2], linewidth=1.5)\n",
    "p10 = results_df['p10']\n",
    "p90 = results_df['p90']\n",
    "plt.fill_between(p10.index, p10, p90, color=colors[2], alpha=0.1, label='80% confidence interval')\n",
    "plt.plot(results_df['p10'], label='Confidence interval lower bound', color=colors[2], linewidth=0.5, alpha=0.5)\n",
    "\n",
    "# And then, add the training period for this item_id:\n",
    "start = pd.to_datetime('2014-09-25 00:00:00')\n",
    "end   = pd.to_datetime('2014-10-02 01:00:00')\n",
    "plt.plot(actual_train_df.loc[start:end, 'actual'], label='Historical data', linewidth=1, color='#AAAAAA', alpha=0.8)\n",
    "\n",
    "# Show the plot with a legend\n",
    "plt.legend(fontsize=14, ncol=5, loc='upper center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New forecast generation strategy\n",
    "To get a new forecast: \n",
    "* Wait 24 hours and update dataset with updated real data. Then, create a new forecast without model retraining.\n",
    "* Train a predictor with a longer forecast horizon (this retrains a model).\n",
    "* Create a forecast and update the dataset with the output of this forecast: then, create a new forecast without model retraining."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup\n",
    "### Helper function to delete Forecast resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wait_till_delete(callback, check_time=5, timeout=None):\n",
    "    elapsed_time = 0\n",
    "    while timeout is None or elapsed_time < timeout:\n",
    "        try:\n",
    "            out = callback()\n",
    "            \n",
    "        except botocore.exceptions.ClientError as e:\n",
    "            # When given the resource not found exception, deletion has occured\n",
    "            if e.response['Error']['Code'] == 'ResourceNotFoundException':\n",
    "                print('Delete successful')\n",
    "                return\n",
    "            \n",
    "            else:\n",
    "                raise\n",
    "                \n",
    "        time.sleep(check_time)\n",
    "        elapsed_time += check_time\n",
    "\n",
    "    raise TimeoutError('Forecast resource deletion timed-out.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the things to cleanup\n",
    "\n",
    "The cells below will delete the items that were created in this notebook one at a time until all items that were created have been removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Delete the Forecast:     ', end='')\n",
    "wait_till_delete(lambda: forecast.delete_forecast(ForecastArn=forecast_arn))\n",
    "\n",
    "print('Delete the Predictor:    ', end='')\n",
    "wait_till_delete(lambda: forecast.delete_predictor(PredictorArn=predictor_arn))\n",
    "\n",
    "print('Delete the Import:       ', end='')\n",
    "wait_till_delete(lambda: forecast.delete_dataset_import_job(DatasetImportJobArn=ds_import_job_arn))\n",
    "\n",
    "print('Delete the Dataset:      ', end='')\n",
    "wait_till_delete(lambda: forecast.delete_dataset(DatasetArn=datasetArn))\n",
    "\n",
    "print('Delete the DatasetGroup: ', end='')\n",
    "wait_till_delete(lambda: forecast.delete_dataset_group(DatasetGroupArn=datasetGroupArn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete your file in S3\n",
    "boto3.Session().resource('s3').Bucket(BUCKET).Object(KEY).delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IAM role and policy cleanup\n",
    "\n",
    "The very last step in the notebooks is to remove the policies that were attached to a role and then to delete it. No changes should need to be made here, just execute the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iam = boto3.client(\"iam\")\n",
    "iam.detach_role_policy(PolicyArn=\"arn:aws:iam::aws:policy/AmazonS3FullAccess\", RoleName=role_name)\n",
    "iam.detach_role_policy(PolicyArn=\"arn:aws:iam::aws:policy/AmazonForecastFullAccess\", RoleName=role_name)\n",
    "iam.delete_role(RoleName=role_name)"
   ]
  }
 ],
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    "name": "ipython",
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