notebooks/Walkthrough.ipynb

{
 "cells": [
  {
   "metadata": {},
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   "source": [
    "# Iowa Liquor Sales Forecast (Walkthrough)\n",
    "\n",
    "This notebook goes through the steps necessary to train and forecast the\n",
    "timeseries models that predict future sales of different liquors for the\n",
    "state of Iowa.\n",
    "\n",
    "## Before You Begin\n",
    "\n",
    "To run this notebook, please make sure you have installed on your Python environment\n",
    "the <span class=\"code\">iowa_forecast</span> package.\n",
    "To install the <span class=\"code\">iowa_forecast</span> package, execute the\n",
    "following command from the project root directory\n",
    "(<span class=\"code\">\"iowa_forecast\"</span>):\n",
    "\n",
    "```console\n",
    "pip install -e .\n",
    "```\n",
    "\n",
    "The above command will install the <code>iowa_forecast</code> package in development \n",
    "mode and the necessary dependencies defined inside the files\n",
    "[pyproject.toml](../pyproject.toml) and [requirements.txt](../requirements.txt).\n",
    "\n",
    "## Running the Whole Process\n",
    "\n",
    "Optionally, you can run the entire training/forecasting process by executing \n",
    "the script [train_model_and_forecast_sales.py](../pipelines/train_model_and_forecast_sales.py):\n",
    "\n",
    "```console\n",
    "# Assuming you're on the project root directory:\n",
    "python pipelines/train_model_and_forecast_sales.py\n",
    "```"
   ],
   "id": "b521c56350baba00"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Import the Necessary Libraries",
   "id": "77e6dd67488554df"
  },
  {
   "cell_type": "code",
   "id": "67bebc49-7da4-4667-aee2-585380ac871f",
   "metadata": {
    "execution": {
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   },
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from google.cloud.bigquery import Client\n",
    "\n",
    "from iowa_forecast.load_data import (create_future_data,\n",
    "                                     create_future_feature_tables,\n",
    "                                     get_training_data)\n",
    "from iowa_forecast.ml_eval import multi_evaluate_predictions\n",
    "from iowa_forecast.ml_train import create_models_for_items, train_arima_models\n",
    "from iowa_forecast.plots import plot_historical_and_forecast\n",
    "from iowa_forecast.utils import (create_bigquery_table_from_pandas,\n",
    "                                 create_dataset_if_not_found)"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Load BigQuery Magic Functions",
   "id": "3744df9a7ab6d77"
  },
  {
   "cell_type": "code",
   "id": "88ec94d2-e08a-4833-a51a-d24ee7353c6c",
   "metadata": {
    "execution": {
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     "start_time": "2024-08-12T22:26:54.074691Z"
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   },
   "source": [
    "%load_ext google.cloud.bigquery"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Set Notebook Package Configurations",
   "id": "8614ed936115ade8"
  },
  {
   "cell_type": "code",
   "id": "46c54e0c-5f6d-490f-a172-79fb8997e90f",
   "metadata": {
    "execution": {
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   },
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "pd.set_option('display.max_columns', None)\n",
    "sns.set_theme()"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Define Notebook Parameters\n",
    "\n",
    "The next cell defines the variables needed to initialize BigQuery client,\n",
    "and the start and end dates of the train, test data."
   ],
   "id": "81980908d7c90d0a"
  },
  {
   "cell_type": "code",
   "id": "23bcc6fc-12e8-4854-884d-6354c99403a5",
   "metadata": {
    "execution": {
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   },
   "source": [
    "how_to_set_environment_variables_message = (\n",
    "    \"To set environment variables:\\n\"\n",
    "    \" - Windows: Use the `set` command in the Command Prompt:\\n\"\n",
    "    \"   ```\\n\"\n",
    "    \"\"\"   echo 'export GOOGLE_APPLICATION_CREDENTIALS=\"/path/to/your/credentials.json\"' >> /etc/profile && source /etc/profile\"\"\"\n",
    "    \"   ```\\n\"\n",
    "    \" - macOS/Linux: Identify which shell you're using by running the \"\n",
    "    \"command `echo $SHELL`. Then depending on whether you're using zsh or \"\n",
    "    \"bash, execute the command in the terminal:\\n\"\n",
    "    \"- For `zsh`:\\n\"\n",
    "    \"   ```\\n\"\n",
    "    \"\"\"   echo 'export GOOGLE_APPLICATION_CREDENTIALS=\"/path/to/your/credentials.json\"' >> ~/.zshrc && source ~/.zshrc\\n\"\"\"\n",
    "    \"   ```\\n\"\n",
    "    \"- For `bash`:\\n\"\n",
    "    \"   ```\\n\"\n",
    "    \"\"\"   echo 'export GOOGLE_APPLICATION_CREDENTIALS=\"/path/to/your/credentials.json\"' >> ~/.bash_profile && source ~/.bash_profile\\n\"\"\"\n",
    "    \"   ```\\n\"\n",
    ")\n",
    "\n",
    "PROJECT_ID = os.environ.get(\"PROJECT_ID\")\n",
    "GOOGLE_APPLICATION_CREDENTIALS = os.environ.get(\"GOOGLE_APPLICATION_CREDENTIALS\")\n",
    "DATASET_NAME = os.environ.get(\"DATASET_NAME\", \"bqmlforecast\")\n",
    "\n",
    "if not any([PROJECT_ID, GOOGLE_APPLICATION_CREDENTIALS]):\n",
    "    raise EnvironmentError(\n",
    "        \"The GOOGLE_APPLICATION_CREDENTIALS and PROJECT_ID environment \"\n",
    "        \"variables are not set.\\nPlease make sure that you have set at least \"\n",
    "        \"one of these environment variables or specify them manually.\\n\"\n",
    "        \"The GOOGLE_APPLICATION_CREDENTIALS should contain the path of your \"\n",
    "        \"Google Cloud credentials JSON file.\\n\"\n",
    "        \"For more information, see: https://cloud.google.com/docs/authentication/getting-started\\n\\n\"\n",
    "        \"Additional context: to generate a new Google Cloud credentials JSON \"\n",
    "        \"file, you need to create a service account in your Google Cloud Project, then\\n\"\n",
    "        \"export the service account credentials as a JSON file.\\n\"\n",
    "        \"Afterward, add the filepath of the downloaded\"\n",
    "        \"JSON credentials to your environment variables.\\n\"\n",
    "        \"To create a new service account, go to: \"\n",
    "        \"https://console.cloud.google.com/iam-admin/serviceaccounts\\n\\n\"\n",
    "        f\"{how_to_set_environment_variables_message}\"\n",
    "    )\n",
    "\n",
    "client_kwargs = {}\n",
    "if isinstance(PROJECT_ID, str):\n",
    "    client_kwargs[\"project\"] = PROJECT_ID\n",
    "\n",
    "if isinstance(GOOGLE_APPLICATION_CREDENTIALS, str):\n",
    "    if not Path(GOOGLE_APPLICATION_CREDENTIALS).is_file():\n",
    "        raise FileNotFoundError(\n",
    "            \"The specified GOOGLE_APPLICATION_CREDENTIALS file at \"\n",
    "            f\"'{GOOGLE_APPLICATION_CREDENTIALS}' was not found.\\n\"\n",
    "            \"Please verify the file path and ensure that the file exists.\\n\"\n",
    "            \"Make sure you have correctly set the GOOGLE_APPLICATION_CREDENTIALS \"\n",
    "            \"environment variable to point to the correct JSON credentials file.\\n\\n\"\n",
    "            f\"{how_to_set_environment_variables_message}\\n\"\n",
    "            \"For more information, see: https://cloud.google.com/docs/authentication/getting-started\\n\"\n",
    "        )\n",
    "    client_kwargs[\"client_options\"] = {\"credentials_file\": GOOGLE_APPLICATION_CREDENTIALS}\n",
    "\n",
    "client = Client(**client_kwargs)\n",
    "\n",
    "END_DATE = pd.Timestamp.today() - pd.DateOffset(days=14)\n",
    "START_DATE = END_DATE - pd.DateOffset(years=3)\n",
    "\n",
    "TEST_START_DATE = END_DATE + pd.DateOffset(days=1)\n",
    "\n",
    "END_DATE = END_DATE.strftime(\"%Y-%m-%d\")\n",
    "START_DATE = START_DATE.strftime(\"%Y-%m-%d\")\n",
    "TEST_START_DATE = TEST_START_DATE.strftime(\"%Y-%m-%d\")\n",
    "HORIZON = 30\n",
    "MAX_ITEMS = 4"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Create a new dataset if it doesn't already exist\n",
    "\n",
    "The function `create_dataset_if_not_found` creates a new dataset with the name\n",
    "specified if it doesn't already exist."
   ],
   "id": "589b6073bb88a7a4"
  },
  {
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   },
   "cell_type": "code",
   "source": "create_dataset_if_not_found(client, dataset_name=DATASET_NAME)",
   "id": "7ddff8af85009f1c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset 'bqmlforecast' already exists.\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Create the Train and Test Tables\n",
    "\n",
    "Next, we'll create the train and test tables using the `get_training_data`\n",
    "function.\n",
    "\n",
    "The `get_training_data` function allows us to specify the starting and ending\n",
    "dates to use when creating the dataset. Therefore, by specifying these\n",
    "parameters, we can create the training and test tables using the same function.\n",
    "Additionally, this function creates or updates the tables to BigQuery.\n",
    "\n",
    "> **Note:** when creating the test dataset, we specify the list of items that\n",
    "> exist in the training data so that both dataframes include information\n",
    "> about the same items."
   ],
   "id": "44a420520c8b69ce"
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   "id": "ba509bdf-959b-4b21-9691-542cbdcace20",
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   },
   "source": [
    "df_train = get_training_data(\n",
    "    client,\n",
    "    table_name=f\"{DATASET_NAME}.training_data\",\n",
    "    start_date=START_DATE,\n",
    "    end_date=END_DATE,\n",
    ")\n",
    "\n",
    "items_list = (\n",
    "    df_train\n",
    "    .groupby(\"item_name\")[\"total_amount_sold\"].sum()\n",
    "    .sort_values(ascending=False).index.to_list()\n",
    ")[:MAX_ITEMS]\n",
    "\n",
    "df_train = get_training_data(\n",
    "    client,\n",
    "    table_name=f\"{DATASET_NAME}.training_data\",\n",
    "    start_date=START_DATE,\n",
    "    end_date=END_DATE,\n",
    "    items_list=items_list,\n",
    ")\n",
    "\n",
    "# When `end_date` is not specified, the function uses today's date as the\n",
    "# default value for the parameter.\n",
    "df_test = get_training_data(\n",
    "    client, start_date=TEST_START_DATE,\n",
    "    table_name=f\"{DATASET_NAME}.test_data\",\n",
    "    items_list=items_list,\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x136797a10>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723501631.121924  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x136772a10>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1723501641.558284  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<google.cloud.bigquery.table._EmptyRowIterator object at 0x136772ad0>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1723501648.304233  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "The above code generates two `pandas.DataFrame` objects each containing the \n",
    "following columns:\n",
    "\n",
    "* **`date`:**\n",
    "    * **Description:** The specific date for each record. It represents the date\n",
    "      on which the sales data was recorded.\n",
    "* **`item_name`:**\n",
    "    * **Description:** The name or description of the item being sold. This is\n",
    "      derived from the `item_description` field in the original Iowa Liquor\n",
    "      sales data.\n",
    "* **`total_amount_sold` (TARGET):**\n",
    "    * **Description:** The total number of bottles sold on the specific date for\n",
    "      the corresponding item. This is the sum of `bottles_sold` aggregated per day.\n",
    "* **`avg_bottle_price`:**\n",
    "    * **Description:** The average retail price of a bottle for the item on the\n",
    "      specific date. This is the average of `state_bottle_retail` prices for\n",
    "      that item.\n",
    "* **`total_volume_sold_liters`:**\n",
    "    * **Description:** The total volume of the item sold in liters on the\n",
    "      specific date. This is calculated as the sum of `volume_sold_liters` for\n",
    "      each day.\n",
    "* **`avg_bottle_cost`:** \n",
    "   * **Description:** The average cost per bottle for the item on the\n",
    "     specific date. This value is derived from the `state_bottle_cost` column\n",
    "     in the sales data and represents the average cost at which the bottles\n",
    "     were purchased by the retailer.\n",
    "\n",
    "* **`day_of_week`:** \n",
    "   * **Description:** The day of the week for the specific date, where 1\n",
    "     represents Sunday, 2 represents Monday, and so on. This is extracted\n",
    "     using the `DAYOFWEEK` function.\n",
    "\n",
    "* **`week_of_year`:** \n",
    "   * **Description:** The week number within the year for the specific date.\n",
    "     This is extracted using the `WEEK` function.\n",
    "\n",
    "* **`month`:** \n",
    "   * **Description:** The month of the year for the specific date. This is\n",
    "     extracted using the `MONTH` function.\n",
    "\n",
    "* **`year`:** \n",
    "    * **Description:** The year for the specific date. This is extracted\n",
    "      using the `YEAR` function.\n",
    "\n",
    "* **`ma3_total_volume_sold_liters`:** \n",
    "    * **Description:** The 3-day moving average of the total volume from the\n",
    "      item sold in liters. This is calculated as the average of the\n",
    "     `total_volume_sold_liters` over the current and preceding two days.\n",
    "\n",
    "* **`ma3_avg_bottle_price`:** \n",
    "    * **Description:** The 3-day moving average of the average retail price\n",
    "      from a bottle for the item. This is calculated as the average of the\n",
    "     `avg_bottle_price` over the current and preceding two days.\n",
    "\n",
    "* **`ma7_total_volume_sold_liters`:** \n",
    "    * **Description:** The 7-day moving average of the total volume from the\n",
    "      item sold in liters. This is calculated as the average of the\n",
    "     `total_volume_sold_liters` over the current and preceding six days.\n",
    "\n",
    "* **`ma7_avg_bottle_price`:** \n",
    "    * **Description:** The 7-day moving average of the average retail price\n",
    "      from a bottle for the item. This is calculated as the average of the\n",
    "     `avg_bottle_price` over the current and preceding six days.\n",
    "\n",
    "* **`ma30_total_volume_sold_liters`:** \n",
    "    * **Description:** The 30-day moving average of the total volume from the\n",
    "      item sold in liters. This is calculated as the average of the\n",
    "     `total_volume_sold_liters` over the current and preceding 29 days.\n",
    "\n",
    "* **`ma30_avg_bottle_price`:** \n",
    "    * **Description:** The 30-day moving average of the average retail price\n",
    "      from a bottle for the item. This is calculated as the average of the\n",
    "     `avg_bottle_price` over the current and preceding 29 days.\n",
    "\n",
    "* **`temperature`:** \n",
    "    * **Description:** The average temperature recorded on the specific date\n",
    "      across various weather stations in Iowa. This is derived from the NOAA\n",
    "      Global Surface Summary of Day (GSOD) data.\n",
    "\n",
    "* **`rainfall`:** \n",
    "    * **Description:** The average amount of rainfall (in inches) recorded on\n",
    "      the specific date across various weather stations in Iowa. This value\n",
    "      is adjusted to replace any missing or erroneous data with zero.\n",
    "\n",
    "* **`snowfall`:** \n",
    "    * **Description:** The average amount of snowfall (in inches) recorded on\n",
    "      the specific date across various weather stations in Iowa. Similar to\n",
    "      rainfall, missing or erroneous data is replaced with zero.\n",
    "\n",
    "* **`lag_1_total_volume_sold_liters`:** \n",
    "    * **Description:** The total volume of the item sold in liters on the\n",
    "     previous day (lagged by 1 day). This feature helps capture short-term\n",
    "     trends in sales volume.\n",
    "\n",
    "* **`lag_7_total_volume_sold_liters`:** \n",
    "    * **Description:** The total volume of the item sold in liters seven days\n",
    "       prior (lagged by 7 days). This feature helps capture weekly seasonality\n",
    "       in sales volume.\n"
   ],
   "id": "a7e23c073201f00e"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Feature ARIMA Models\n",
    "\n",
    "Since we don't have our model's features values during forecast, we need to \n",
    "somehow estimate their values and use these estimates to predict future sales.\n",
    "The following code trains uni-variate ARIMA models and creates tables with\n",
    "predicted values for each of our \"base\" features.\n",
    "\n",
    "> **Note:** Our final model contains additional features, but they are all\n",
    "> byproducts of these three columns defined inside the `columns` list."
   ],
   "id": "a91d2fc906f5a1ca"
  },
  {
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   },
   "cell_type": "code",
   "source": [
    "columns = [\"avg_bottle_price\", \"avg_bottle_cost\", \"total_volume_sold_liters\"]\n",
    "\n",
    "train_arima_models(\n",
    "    client,\n",
    "    columns,\n",
    "    model=f\"{PROJECT_ID}.{DATASET_NAME}.arima_model\",\n",
    "    train_table_name=f\"{PROJECT_ID}.{DATASET_NAME}.training_data\",\n",
    "    test_table_name=f\"{PROJECT_ID}.{DATASET_NAME}.test_data\",\n",
    "    model_metrics_table_name=f\"{PROJECT_ID}.{DATASET_NAME}.arima_model_metrics\",\n",
    "    time_series_timestamp_col=\"date\",\n",
    "    time_series_id_col=\"item_name\",\n",
    "    confidence_level=0.9,\n",
    "    horizon=HORIZON,\n",
    "    use_test_data_on_train=False,\n",
    ")\n",
    "\n",
    "create_future_feature_tables(\n",
    "    client,\n",
    "    columns=columns,\n",
    "    model=f\"{PROJECT_ID}.{DATASET_NAME}.arima_model\",\n",
    "    table_base_name=f\"{PROJECT_ID}.{DATASET_NAME}.forecast\",\n",
    "    confidence_level=0.9,\n",
    "    horizon=HORIZON,\n",
    ")"
   ],
   "id": "16c724cdb1e7b5a0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ]
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ]
     },
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    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Create a Table for Future Sales Predictions\n",
    "\n",
    "Next, we'll create/update a table inside BigQuery with future dates and the \n",
    "predicted values of our features from our trained ARIMA models."
   ],
   "id": "f1182a34e89372dc"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-12T22:28:57.574343Z",
     "start_time": "2024-08-12T22:28:49.250050Z"
    }
   },
   "cell_type": "code",
   "source": [
    "create_future_data(\n",
    "    client=client,\n",
    "    train_table_name=f\"{PROJECT_ID}.{DATASET_NAME}.training_data\",\n",
    "    test_table_name=f\"{PROJECT_ID}.{DATASET_NAME}.test_data\",\n",
    "    forecast_table_name=f\"{PROJECT_ID}.{DATASET_NAME}.future_forecast_data\",\n",
    "    horizon=HORIZON,\n",
    "    dataset_id=DATASET_NAME,\n",
    ")"
   ],
   "id": "76a7bcee9c41fe4",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1723501730.330978  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "id": "64fbfe97-546e-460c-91cf-e57fb38802e1",
   "metadata": {},
   "source": [
    "## Train Sales Forecasting Models\n",
    "\n",
    "The function `create_models_for_items` trains a multivariate ARIMA model for each `'item_name'` specified in the `items_list` parameter.\n",
    "By default, the models are saved as `'bqmlforecast.arima_plus_xreg_model_<NAME OF THE ITEM>'`.\n",
    "\n",
    "**For example:** <code>bqmlforecast.arima_plus_xreg_model_<b>black_velvet</b></code>."
   ]
  },
  {
   "cell_type": "code",
   "id": "9dbc1efc-eb17-4ab2-ba7b-2837301d6774",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-12T22:30:54.693995Z",
     "start_time": "2024-08-12T22:29:23.345278Z"
    }
   },
   "source": [
    "create_models_for_items(\n",
    "    client,\n",
    "    items_list,\n",
    "    max_items=MAX_ITEMS,\n",
    "    clean_spikes_and_dips=True,\n",
    "    test_table_name=None,\n",
    ")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Output()"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ]
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    },
    {
     "data": {
      "text/plain": [
       "\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Forecast Future Sales and Evaluate the Models",
   "id": "f7aae4956c0eadce"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-12T22:33:51.198438Z",
     "start_time": "2024-08-12T22:33:15.204713Z"
    }
   },
   "cell_type": "code",
   "source": [
    "forecast_dict = multi_evaluate_predictions(\n",
    "    client,\n",
    "    items_list[:MAX_ITEMS],\n",
    "    confidence_level=0.9,\n",
    "    horizon=HORIZON,\n",
    "    forecast_table_name=f\"{PROJECT_ID}.{DATASET_NAME}.future_forecast_data\",\n",
    ")"
   ],
   "id": "409a89e2dfa3c892",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Output()"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "34cc8bfbc5b94d0fae4b9aee7dd9179b"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1723501996.467930  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723501999.641256  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502002.647667  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502005.389530  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502008.272938  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502011.927910  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502014.797378  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502017.566189  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502020.571530  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502023.502798  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502026.267326  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n",
      "I0000 00:00:1723502029.515344  822971 check_gcp_environment_no_op.cc:29] ALTS: Platforms other than Linux and Windows are not supported\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "9fc07352-5a53-42e7-95bb-aaff4eca0926",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-08T01:10:37.320062Z",
     "iopub.status.busy": "2024-08-08T01:10:37.319628Z",
     "iopub.status.idle": "2024-08-08T01:11:18.524454Z",
     "shell.execute_reply": "2024-08-08T01:11:18.523350Z",
     "shell.execute_reply.started": "2024-08-08T01:10:37.320030Z"
    },
    "ExecuteTime": {
     "end_time": "2024-08-12T22:36:28.500779Z",
     "start_time": "2024-08-12T22:36:19.200229Z"
    }
   },
   "source": [
    "items_predictions = []\n",
    "for item_name, forecast_info in forecast_dict.items():\n",
    "    training_df = forecast_info[\"train_df\"].sort_values(\"date\").assign(\n",
    "        **{\n",
    "            \"forecast_value\": 0,\n",
    "            \"prediction_interval_lower_bound\": 0,\n",
    "            \"prediction_interval_upper_bound\": 0,\n",
    "            \"confidence_level\": 0,\n",
    "            \"time_series_type\": \"history\",\n",
    "        }\n",
    "    )\n",
    "    predictions_df = forecast_info[\"eval_df\"].sort_values(\"date\").assign(\n",
    "        **{\"time_series_type\": \"forecast\"}\n",
    "    )\n",
    "    items_predictions = [*items_predictions, training_df, predictions_df]\n",
    "    plot_historical_and_forecast(\n",
    "        input_timeseries=training_df,\n",
    "        timestamp_col_name=\"date\",\n",
    "        data_col_name=\"total_amount_sold\",\n",
    "        forecast_output=predictions_df,\n",
    "        actual=predictions_df,\n",
    "        title=f\"Item: {item_name}\",\n",
    "        plotstartdate=(pd.to_datetime(END_DATE) - pd.DateOffset(months=2)).strftime(\"%Y-%m-%d\"),\n",
    "        prop={'size': 12},\n",
    "        engine=\"plotly\",\n",
    "    )\n",
    "\n",
    "items_predictions_df = (\n",
    "    pd.concat(items_predictions, ignore_index=True)\n",
    "    .sort_values([\"item_name\", \"date\"])\n",
    ")\n",
    "items_predictions_df = items_predictions_df.fillna(0).astype(\n",
    "    {\n",
    "        col: str for col in\n",
    "        items_predictions_df.select_dtypes(include=object).columns\n",
    "    }\n",
    ")\n",
    "create_bigquery_table_from_pandas(\n",
    "    client,\n",
    "    items_predictions_df,\n",
    "    \"predictions_table\",\n",
    "    DATASET_NAME,\n",
    ")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/_plotly_utils/basevalidators.py:106: FutureWarning:\n",
      "\n",
      "The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
      "\n",
      "/usr/local/lib/python3.11/site-packages/_plotly_utils/basevalidators.py:106: FutureWarning:\n",
      "\n",
      "The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
      "\n",
      "/usr/local/lib/python3.11/site-packages/_plotly_utils/basevalidators.py:106: FutureWarning:\n",
      "\n",
      "The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
      "\n",
      "/usr/local/lib/python3.11/site-packages/_plotly_utils/basevalidators.py:106: FutureWarning:\n",
      "\n",
      "The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
      "\n"
     ]
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     "data": {
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