TimeSeries_M5/Forecasting_M5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14f5686c-449b-4376-8c58-fc8141f4b0f8",
   "metadata": {},
   "source": [
    "# Multi Series Forecasting with Statistical and Machine Learning using Snowflake"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3de61b5",
   "metadata": {},
   "source": [
    "Outline:\n",
    "\n",
    "* Reading Data: Start with the public M5 dataset, prepare it and store it in Snowflake\n",
    "\n",
    "* Forecasting Using Statistical and ML: We apply a wide range of forecasting methods from basic statistical techniques to ML models. The predictions are done in the local environment of the notebook, but then stored in Snowflake. I also show how to use a UDTF to parralelize the predictions across a Snowflake warehouse. Because of the small data size, there is no advantage to using the UDTF.\n",
    "\n",
    "* Model Performance Evaluation on Different Windows: We assess the performance at the level of individual time series and aggreggate performance across all the time series in the local environment. The results are stored in Snowflake\n",
    "\n",
    "* Visualizations: Pulling the data from snowflake, we use standard python libraries to visualize the training data and evaluation results. A companion notebook has a streamlit application that can be used to visualize the results. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36eb23c-110c-47f6-bc2c-bb2c5e224ab4",
   "metadata": {},
   "source": [
    "Background: \n",
    "This notebook forecasts using the M5 dataset. The M5 dataset comprises of hierarchical sales data, spanning five years, from Walmart. The aim is to forecast daily sales for the next 28 days. The dataset is broken down into the 50 states of America, with 10 stores in each state."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775c9447-1248-43f1-ade0-7903e8bd3ce3",
   "metadata": {},
   "source": [
    "In this notebook, we aim to provide a more structured, data-driven approach to model selection for different groups of series within the M5 benchmark dataset. This dataset, well-known in the field of forecasting, allows us to showcase the versatility and power of our methodology.\n",
    "\n",
    "We will train an assortment of models from various forecasting paradigms:\n",
    "\n",
    "*[StatsForecast]((https://github.com/Nixtla/statsforecast))*\n",
    "\n",
    "- Baseline models: These models are simple yet often highly effective for providing an initial perspective on the forecasting problem. We will use `SeasonalNaive` and `HistoricAverage` models for this category.\n",
    "- Intermittent models: For series with sporadic, non-continuous demand, we will utilize models like `CrostonOptimized`, `IMAPA`, and `ADIDA`. These models are particularly suited for handling zero-inflated series.\n",
    "- State Space Models: These are statistical models that use mathematical descriptions of a system to make predictions. The `AutoETS` model from the statsforecast library falls under this category.\n",
    "\n",
    "*[MLForecast](https://github.com/Nixtla/mlforecast)*\n",
    "\n",
    "Machine Learning: Leveraging ML models like `LightGBM`, `XGBoost`, and `LinearRegression` can be advantageous due to their capacity to uncover intricate patterns in data. We'll use the MLForecast library for this purpose.\n",
    "\n",
    "The original version of this notebook is from Nixtla and found [here](https://nixtlaverse.nixtla.io/neuralforecast/examples/statsmlneuralmethods.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c8fa3bf-ed6f-4a2a-a6fc-d1fd14dba8c1",
   "metadata": {},
   "source": [
    "## 1. Setup Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40ca2a7b-ff16-4ada-aaa5-b5146770ec9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "#!pip install statsforecast mlforecast datasetforecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2edce364-4709-4c06-a001-9234bba09568",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Snowflake connector\n",
    "from snowflake import connector\n",
    "#from snowflake.ml.utils import connection_params\n",
    "\n",
    "# Snowpark for Python\n",
    "from snowflake.snowpark.session import Session\n",
    "from snowflake.snowpark.types import Variant\n",
    "from snowflake.snowpark.version import VERSION\n",
    "from snowflake.snowpark import functions as F\n",
    "from snowflake.snowpark.types import *\n",
    "\n",
    "# Snowpark ML\n",
    "from snowflake.ml._internal import env as snowml_env\n",
    "from snowflake.ml.modeling.compose import ColumnTransformer\n",
    "from snowflake.ml.modeling.pipeline import Pipeline\n",
    "from snowflake.ml.modeling.preprocessing import StandardScaler, OrdinalEncoder\n",
    "from snowflake.ml.modeling.impute import SimpleImputer\n",
    "from snowflake.ml.modeling.model_selection import GridSearchCV\n",
    "from snowflake.ml.modeling.xgboost import XGBRegressor\n",
    "\n",
    "# Misc\n",
    "import pandas as pd\n",
    "import json\n",
    "import logging \n",
    "logger = logging.getLogger(\"snowflake.snowpark.session\")\n",
    "logger.setLevel(logging.ERROR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24786464",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../../creds.json') as f:\n",
    "    data = json.load(f)\n",
    "    USERNAME = data['user']\n",
    "    PASSWORD = data['password']\n",
    "    SF_ACCOUNT = data['account']\n",
    "    SF_WH = data['warehouse']\n",
    "\n",
    "CONNECTION_PARAMETERS = {\n",
    "   \"account\": SF_ACCOUNT,\n",
    "   \"user\": USERNAME,\n",
    "   \"password\": PASSWORD,\n",
    "}\n",
    "\n",
    "session = Session.builder.configs(CONNECTION_PARAMETERS).create()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c58fab92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User                        : RSHAH\n",
      "Role                        : \"RAJIV\"\n",
      "Database                    : \"RAJIV\"\n",
      "Schema                      : \"PUBLIC\"\n",
      "Warehouse                   : \"RAJIV\"\n",
      "Snowflake version           : 8.9.1\n",
      "Snowpark for Python version : 1.11.1\n",
      "Snowflake Python ML version : 1.2.2\n"
     ]
    }
   ],
   "source": [
    "snowflake_environment = session.sql('select current_user(), current_version()').collect()\n",
    "snowpark_version = VERSION\n",
    "\n",
    "# Current Environment Details\n",
    "print('User                        : {}'.format(snowflake_environment[0][0]))\n",
    "print('Role                        : {}'.format(session.get_current_role()))\n",
    "print('Database                    : {}'.format(session.get_current_database()))\n",
    "print('Schema                      : {}'.format(session.get_current_schema()))\n",
    "print('Warehouse                   : {}'.format(session.get_current_warehouse()))\n",
    "print('Snowflake version           : {}'.format(snowflake_environment[0][1]))\n",
    "print('Snowpark for Python version : {}.{}.{}'.format(snowpark_version[0],snowpark_version[1],snowpark_version[2]))\n",
    "print('Snowflake Python ML version : {}'.format(snowml_env.VERSION))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb93f67b",
   "metadata": {},
   "source": [
    "## 2. Get the Data\n",
    "The example uses the [M5 dataset](https://github.com/Mcompetitions/M5-methods/blob/master/M5-Competitors-Guide.pdf). It consists of `30,490` bottom time series.\n",
    "\n",
    "I have write the data to a Snowflake table, but the first time you will need to uncomment the below cell and get the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7716b9aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "## First time you will need to uncomment this part\n",
    "## Load the training target dataset from the provided URL\n",
    "#Y_df = pd.read_parquet('https://m5-benchmarks.s3.amazonaws.com/data/train/target.parquet')\n",
    "#Y_df.columns = Y_df.columns.str.upper()\n",
    "#input_df = session.create_dataframe(Y_df)\n",
    "#input_df.write.mode('overwrite').save_as_table('TS_M5_dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c816bafe",
   "metadata": {},
   "source": [
    "Now let's pull the data from Snowflake, using a Snowflake dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9b8c7366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------\n",
      "|\"ITEM_ID\"         |\"TIMESTAMP\"  |\"DEMAND\"  |\n",
      "---------------------------------------------\n",
      "|FOODS_3_374_TX_2  |2012-10-20   |1.0       |\n",
      "|FOODS_3_374_TX_2  |2012-10-21   |0.0       |\n",
      "|FOODS_3_374_TX_2  |2012-10-22   |0.0       |\n",
      "|FOODS_3_374_TX_2  |2012-10-23   |0.0       |\n",
      "|FOODS_3_374_TX_2  |2012-10-24   |0.0       |\n",
      "|FOODS_3_374_TX_2  |2012-10-25   |0.0       |\n",
      "|FOODS_3_374_TX_2  |2012-10-26   |0.0       |\n",
      "|FOODS_3_374_TX_2  |2012-10-27   |0.0       |\n",
      "|FOODS_3_374_TX_2  |2012-10-28   |1.0       |\n",
      "|FOODS_3_374_TX_2  |2012-10-29   |2.0       |\n",
      "---------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = session.read.table('TS_M5_dataset')\n",
    "df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3414d396",
   "metadata": {},
   "source": [
    "## 3. Data Preprocessing\n",
    "\n",
    "This section takes the large training dataset, does some slight cleanup, and filters it down to a smaller subset for this notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fbd3b9db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------\n",
      "|\"UNIQUE_ID\"       |\"Y\"  |\"DS\"        |\n",
      "---------------------------------------\n",
      "|FOODS_1_001_CA_1  |3.0  |2011-01-29  |\n",
      "|FOODS_1_001_CA_1  |0.0  |2011-01-30  |\n",
      "|FOODS_1_001_CA_1  |0.0  |2011-01-31  |\n",
      "|FOODS_1_001_CA_1  |1.0  |2011-02-01  |\n",
      "|FOODS_1_001_CA_1  |4.0  |2011-02-02  |\n",
      "|FOODS_1_001_CA_1  |2.0  |2011-02-03  |\n",
      "|FOODS_1_001_CA_1  |0.0  |2011-02-04  |\n",
      "|FOODS_1_001_CA_1  |2.0  |2011-02-05  |\n",
      "|FOODS_1_001_CA_1  |0.0  |2011-02-06  |\n",
      "|FOODS_1_001_CA_1  |0.0  |2011-02-07  |\n",
      "---------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from snowflake.snowpark.functions import col, to_date\n",
    "\n",
    "# Rename columns to match the Nixtlaverse's expectations\n",
    "# The 'item_id' becomes 'unique_id' representing the unique identifier of the time series\n",
    "# The 'timestamp' becomes 'ds' representing the time stamp of the data points\n",
    "# The 'demand' becomes 'y' representing the target variable we want to forecast\n",
    "\n",
    "# Renaming columns\n",
    "df = df.withColumnRenamed(\"item_id\", \"unique_id\") \\\n",
    "       .withColumnRenamed(\"timestamp\", \"ds\") \\\n",
    "       .withColumnRenamed(\"demand\", \"y\")\n",
    "\n",
    "# Convert 'ds' column to datetime format\n",
    "df = df.withColumn(\"ds\", col(\"ds\").cast(\"date\"))\n",
    "\n",
    "# To get the number of rows, use df.count()\n",
    "num_rows = df.count()\n",
    "\n",
    "# To get the number of columns, use len(df.columns)\n",
    "num_columns = len(df.columns)\n",
    "\n",
    "# Shape-like information (num_rows, num_columns)\n",
    "shape_like_info = (num_rows, num_columns)\n",
    "\n",
    "df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef96650",
   "metadata": {},
   "source": [
    "Get the size of the dataset - 47M rows!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4a3328a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46796220, 3)\n"
     ]
    }
   ],
   "source": [
    "# To get the number of rows, use df.count()\n",
    "num_rows = df.count()\n",
    "\n",
    "# To get the number of columns, use len(df.columns)\n",
    "num_columns = len(df.columns)\n",
    "\n",
    "# Shape-like information (num_rows, num_columns)\n",
    "shape_like_info = (num_rows, num_columns)\n",
    "print (shape_like_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a6f1a46-19f7-4cf9-b7a4-f05362448917",
   "metadata": {},
   "source": [
    "For simplicity sake we will keep just one category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d051e09e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 12555448\n",
      "[Row(UNIQUE_ID='FOODS_3_374_TX_2', Y=1.0, DS=datetime.date(2012, 10, 20)), Row(UNIQUE_ID='FOODS_3_374_TX_2', Y=0.0, DS=datetime.date(2012, 10, 21)), Row(UNIQUE_ID='FOODS_3_374_TX_2', Y=0.0, DS=datetime.date(2012, 10, 22)), Row(UNIQUE_ID='FOODS_3_374_TX_2', Y=0.0, DS=datetime.date(2012, 10, 23)), Row(UNIQUE_ID='FOODS_3_374_TX_2', Y=0.0, DS=datetime.date(2012, 10, 24))]\n",
      "The number of distinct IDs is: 8230\n"
     ]
    }
   ],
   "source": [
    "from snowflake.snowpark.functions import col, count, lit\n",
    "\n",
    "# Filter rows to one category\n",
    "df = df.filter(col(\"unique_id\").like(\"FOODS_3%\"))\n",
    "\n",
    "print(f\"Number of rows: {df.count()}\")\n",
    "\n",
    "#Print the first few rows of the DataFrame\n",
    "print(df.limit(5).collect())\n",
    "\n",
    "#Count distinct unique_id\n",
    "number_of_distinct_ids = df.select(col(\"unique_id\")).distinct().count()\n",
    "print(f\"The number of distinct IDs is: {number_of_distinct_ids}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "722c97ea",
   "metadata": {},
   "source": [
    "For computation, let's reduce this down to 100 series for initial testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18e3e148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 30847\n",
      "---------------------------------------\n",
      "|\"UNIQUE_ID\"       |\"Y\"  |\"DS\"        |\n",
      "---------------------------------------\n",
      "|FOODS_3_386_TX_3  |1.0  |2012-05-11  |\n",
      "|FOODS_3_386_TX_3  |0.0  |2012-05-12  |\n",
      "|FOODS_3_386_TX_3  |0.0  |2012-05-13  |\n",
      "|FOODS_3_386_TX_3  |0.0  |2012-05-14  |\n",
      "|FOODS_3_386_TX_3  |0.0  |2012-05-15  |\n",
      "|FOODS_3_386_TX_3  |0.0  |2012-05-16  |\n",
      "|FOODS_3_386_TX_3  |0.0  |2012-05-17  |\n",
      "|FOODS_3_386_TX_3  |1.0  |2012-05-18  |\n",
      "|FOODS_3_386_TX_3  |0.0  |2012-05-19  |\n",
      "|FOODS_3_386_TX_3  |0.0  |2012-05-20  |\n",
      "---------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nseries = 20\n",
    "\n",
    "# Extract the unique IDs\n",
    "unique_ids_df = df.select(\"unique_id\").distinct()\n",
    "\n",
    "# Collect them into a Pandas DataFrame\n",
    "unique_ids_pandas_df = unique_ids_df.toPandas()\n",
    "\n",
    "# Sample 500 unique IDs\n",
    "sampled_ids_pandas_df = unique_ids_pandas_df.sample(n=nseries, random_state=1)\n",
    "\n",
    "# Extract the list of sampled IDs\n",
    "sampled_ids_list = sampled_ids_pandas_df['UNIQUE_ID'].tolist()\n",
    "\n",
    "# Filter the Snowpark DataFrame based on the sampled IDs\n",
    "df_filtered = df.filter(col(\"unique_id\").isin(sampled_ids_list))\n",
    "\n",
    "print(f\"Number of rows: {df_filtered.count()}\")\n",
    "\n",
    "df_filtered.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63952f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered.write.mode('overwrite').save_as_table('TS_M5_train')\n",
    "df_filtered = session.read.table('TS_M5_train')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c126ec1-cabb-4963-9427-7e4c98740e47",
   "metadata": {},
   "source": [
    "## 4. Training with StatsForecast"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "129f0abe-f405-473f-b06d-dea529cffb56",
   "metadata": {},
   "source": [
    "`StatsForecast` is a comprehensive library providing a suite of popular univariate time series forecasting models, all designed with a focus on high performance and scalability.\n",
    "\n",
    "`StatsForecast` receives a list of models to fit each time series. Since we are dealing with Daily data, it would be benefitial to use 7 as seasonality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f270278d-e351-4445-8462-7ab6e3eef9e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajishah/anaconda3/envs/working38/lib/python3.8/site-packages/statsforecast/core.py:21: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "# Import necessary models from the statsforecast library\n",
    "from statsforecast.models import (\n",
    "    # SeasonalNaive: A model that uses the previous season's data as the forecast\n",
    "    SeasonalNaive,\n",
    "    # Naive: A simple model that uses the last observed value as the forecast\n",
    "    Naive,\n",
    "    # HistoricAverage: This model uses the average of all historical data as the forecast\n",
    "    HistoricAverage,\n",
    "    # CrostonOptimized: A model specifically designed for intermittent demand forecasting\n",
    "    CrostonOptimized,\n",
    "    # ADIDA: Adaptive combination of Intermittent Demand Approaches, a model designed for intermittent demand\n",
    "    ADIDA,\n",
    "    # IMAPA: Intermittent Multiplicative AutoRegressive Average, a model for intermittent series that incorporates autocorrelation\n",
    "    IMAPA,\n",
    "    # AutoETS: Automated Exponential Smoothing model that automatically selects the best Exponential Smoothing model based on AIC\n",
    "    AutoETS\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ae7c2d-fd86-4eef-aeb7-9a70dd41b24d",
   "metadata": {},
   "source": [
    "We fit the models by instantiating a new StatsForecast object with the following parameters:\n",
    "\n",
    "- `models`: a list of models. Select the models you want from models and import them.\n",
    "- `freq`: a string indicating the frequency of the data. (See panda’s available frequencies.)\n",
    "- `n_jobs`: int, number of jobs used in the parallel processing, use -1 for all cores.\n",
    "- `fallback_model`: a model to be used if a model fails.\n",
    "Any settings are passed into the constructor. Then you call its fit method and pass in the historical data frame.\n",
    "\n",
    "The forecast method takes two arguments: forecasts next h (horizon) and level.\n",
    "\n",
    "- `h` (int): represents the forecast h steps into the future. In this case, 12 months ahead.\n",
    "- `level` (list of floats): this optional parameter is used for probabilistic forecasting. Set the level (or confidence percentile) of your prediction interval. For example, level=[90] means that the model expects the real value to be inside that interval 90% of the times."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36132c20",
   "metadata": {},
   "source": [
    "For this training run, we will use a horizon of 28 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "94651b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "horizon = 28"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230294b4",
   "metadata": {},
   "source": [
    "Let's create a dataframe for training dataset with the last 28 days to use for training the model. The last 28 days will be used for validation. This is a very simple approach, for produciton use, you will probably want to use time series cross validation with multiple validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2f57950",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------\n",
      "|\"UNIQUE_ID\"       |\"Y\"  |\"DS\"        |\n",
      "---------------------------------------\n",
      "|FOODS_3_020_WI_1  |1.0  |2011-01-29  |\n",
      "|FOODS_3_020_WI_1  |1.0  |2011-01-30  |\n",
      "|FOODS_3_020_WI_1  |1.0  |2011-01-31  |\n",
      "|FOODS_3_020_WI_1  |0.0  |2011-02-01  |\n",
      "|FOODS_3_020_WI_1  |0.0  |2011-02-02  |\n",
      "|FOODS_3_020_WI_1  |1.0  |2011-02-03  |\n",
      "|FOODS_3_020_WI_1  |0.0  |2011-02-04  |\n",
      "|FOODS_3_020_WI_1  |0.0  |2011-02-05  |\n",
      "|FOODS_3_020_WI_1  |0.0  |2011-02-06  |\n",
      "|FOODS_3_020_WI_1  |0.0  |2011-02-07  |\n",
      "---------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from snowflake.snowpark.functions import max, date_add, col\n",
    "\n",
    "# Assume `df` is your Snowpark DataFrame\n",
    "# Calculate the maximum date for each UNIQUE_ID\n",
    "max_date_df = df_filtered.groupBy(\"UNIQUE_ID\").agg(max(\"DS\").alias(\"MaxDate\"))\n",
    "\n",
    "# Join the max date back to the original dataframe to associate each row with its series' max date\n",
    "df_with_max_date = df.join(max_date_df, \"UNIQUE_ID\")\n",
    "\n",
    "# Filter out the rows where the date is within the last 30 days of the maximum date\n",
    "train_sample = df_with_max_date.filter(col(\"DS\") <= date_add(col(\"MaxDate\"), -horizon))\n",
    "\n",
    "# Now drop the MaxDate column\n",
    "train_sample = train_sample.drop(\"MaxDate\")\n",
    "\n",
    "train_sample.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28cf55df",
   "metadata": {},
   "source": [
    "Pull it in locally and start training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c10ce0f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>unique_id</th>\n",
       "      <th>y</th>\n",
       "      <th>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-05-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          unique_id    y         ds\n",
       "0  FOODS_3_386_TX_3  1.0 2012-05-11\n",
       "1  FOODS_3_386_TX_3  0.0 2012-05-12\n",
       "2  FOODS_3_386_TX_3  0.0 2012-05-13\n",
       "3  FOODS_3_386_TX_3  0.0 2012-05-14\n",
       "4  FOODS_3_386_TX_3  0.0 2012-05-15"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_sample.toPandas()\n",
    "train_df.columns = ['unique_id', 'y', 'ds']\n",
    "train_df['ds'] = pd.to_datetime(train_df['ds'])\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad0b66a7-4b96-4cb6-a220-45f45d7bb7b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n",
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n",
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n",
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n",
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n",
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n",
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n",
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n"
     ]
    }
   ],
   "source": [
    "#from statsforecast.utils import ConformalIntervals \n",
    "\n",
    "# intervals = ConformalIntervals(h=horizon, n_windows=2)\n",
    "# models = [\n",
    "#    SeasonalNaive(season_length=7,prediction_intervals=intervals),\n",
    "#    Naive(prediction_intervals=intervals),\n",
    "#    HistoricAverage(prediction_intervals=intervals),\n",
    "#    CrostonOptimized(prediction_intervals=intervals),\n",
    "#    ADIDA(prediction_intervals=intervals),\n",
    "#    IMAPA(prediction_intervals=intervals),\n",
    "#    AutoETS(season_length=7,prediction_intervals=intervals)\n",
    "#]\n",
    "\n",
    "models = [\n",
    "    SeasonalNaive(season_length=7),\n",
    "    Naive(),\n",
    "    HistoricAverage(),\n",
    "    CrostonOptimized(),\n",
    "    ADIDA(),\n",
    "    IMAPA(),\n",
    "    AutoETS(season_length=7)\n",
    "]\n",
    "\n",
    "# Instantiate the StatsForecast class\n",
    "from statsforecast import StatsForecast\n",
    "sf = StatsForecast(\n",
    "    models=models,  # A list of models to be used for forecasting\n",
    "    freq='D',  # The frequency of the time series data (in this case, 'D' stands for daily frequency)\n",
    "    n_jobs=-1,  # The number of CPU cores to use for parallel execution (-1 means use all available cores)\n",
    ")\n",
    "\n",
    "fcst_df = sf.forecast(df=train_df, h=28)\n",
    "\n",
    "fcst_df = fcst_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea6767d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          UNIQUE_ID                   DS      ALGORITHM  VALUE\n",
      "0  FOODS_3_020_WI_1  2016-04-25 00:00:00  SeasonalNaive    2.0\n",
      "1  FOODS_3_020_WI_1  2016-04-26 00:00:00  SeasonalNaive    5.0\n",
      "2  FOODS_3_020_WI_1  2016-04-27 00:00:00  SeasonalNaive    0.0\n",
      "3  FOODS_3_020_WI_1  2016-04-28 00:00:00  SeasonalNaive    0.0\n",
      "4  FOODS_3_020_WI_1  2016-04-29 00:00:00  SeasonalNaive    3.0\n"
     ]
    }
   ],
   "source": [
    "melted_df = pd.melt(fcst_df, id_vars=['unique_id', 'ds'], var_name='ALGORITHM', value_name='VALUE')\n",
    "\n",
    "# Now you have a DataFrame with a 'UNIQUE_ID', 'DS', 'Algorithm', and 'Value' column\n",
    "\n",
    "melted_df.columns = melted_df.columns.str.upper()\n",
    "melted_df['DS'] = melted_df['DS'].dt.strftime('%Y-%m-%d %H:%M:%S')\n",
    "print(melted_df.head())\n",
    "input_df = session.create_dataframe(melted_df)\n",
    "input_df.write.mode('overwrite').save_as_table('TS_M5_forecast_Stats')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb8e1602-dc44-4223-9eeb-79d7a778e483",
   "metadata": {},
   "source": [
    "## MLForecast"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6127e0-093d-45b4-8425-883dffb2760d",
   "metadata": {},
   "source": [
    "`MLForecast` is a powerful library that provides automated feature creation for time series forecasting, facilitating the use of global machine learning models. It is designed for high performance and scalability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "64004602-9d73-4675-a44a-23f3afb55ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlforecast import MLForecast\n",
    "from mlforecast.target_transforms import Differences\n",
    "from mlforecast.utils import PredictionIntervals\n",
    "from window_ops.expanding import expanding_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "918b50d6-bf2f-4835-9bcf-412c1722c2de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%capture\n",
    "#!pip install lightgbm xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6819fb98-7843-4c29-8b41-4e2e2f8e3d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary models from various libraries\n",
    "\n",
    "# LGBMRegressor: A gradient boosting framework that uses tree-based learning algorithms from the LightGBM library\n",
    "#from lightgbm import LGBMRegressor\n",
    "\n",
    "# XGBRegressor: A gradient boosting regressor model from the XGBoost library\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "# LinearRegression: A simple linear regression model from the scikit-learn library\n",
    "from sklearn.linear_model import LinearRegression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dddee356-0886-4bc5-adfc-bf46833d3daf",
   "metadata": {},
   "source": [
    "To use `MLForecast` for time series forecasting, we instantiate a new `MLForecast` object and provide it with various parameters to tailor the modeling process to our specific needs:\n",
    "\n",
    "- `models`: This parameter accepts a list of machine learning models you wish to use for forecasting. You can import your preferred models from scikit-learn, lightgbm and xgboost.\n",
    "\n",
    "- `freq`: This is a string indicating the frequency of your data (hourly, daily, weekly, etc.). The specific format of this string should align with pandas' recognized frequency strings.\n",
    "\n",
    "- `target_transforms`: These are transformations applied to the target variable before model training and after model prediction. This can be useful when working with data that may benefit from transformations, such as log-transforms for highly skewed data.\n",
    "\n",
    "- `lags`: This parameter accepts specific lag values to be used as regressors. Lags represent how many steps back in time you want to look when creating features for your model. For example, if you want to use the previous day's data as a feature for predicting today's value, you would specify a lag of 1.\n",
    "\n",
    "- `lags_transforms`: These are specific transformations for each lag. This allows you to apply transformations to your lagged features.\n",
    "\n",
    "- `date_features`: This parameter specifies date-related features to be used as regressors. For instance, you might want to include the day of the week or the month as a feature in your model.\n",
    "\n",
    "- `num_threads`: This parameter controls the number of threads to use for parallelizing feature creation, helping to speed up this process when working with large datasets.\n",
    "\n",
    "All these settings are passed to the `MLForecast` constructor. Once the `MLForecast` object is initialized with these settings, we call its `fit` method and pass the historical data frame as the argument. The `fit` method trains the models on the provided historical data, readying them for future forecasting tasks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7fcfb2c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>y</th>\n",
       "      <th>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-05-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_3_386_TX_3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30282</th>\n",
       "      <td>FOODS_3_349_CA_1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2016-04-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30283</th>\n",
       "      <td>FOODS_3_349_CA_1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2016-04-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30284</th>\n",
       "      <td>FOODS_3_349_CA_1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2016-04-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30285</th>\n",
       "      <td>FOODS_3_349_CA_1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2016-04-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30286</th>\n",
       "      <td>FOODS_3_349_CA_1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-04-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30287 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              unique_id     y         ds\n",
       "0      FOODS_3_386_TX_3   1.0 2012-05-11\n",
       "1      FOODS_3_386_TX_3   0.0 2012-05-12\n",
       "2      FOODS_3_386_TX_3   0.0 2012-05-13\n",
       "3      FOODS_3_386_TX_3   0.0 2012-05-14\n",
       "4      FOODS_3_386_TX_3   0.0 2012-05-15\n",
       "...                 ...   ...        ...\n",
       "30282  FOODS_3_349_CA_1   5.0 2016-04-20\n",
       "30283  FOODS_3_349_CA_1   8.0 2016-04-21\n",
       "30284  FOODS_3_349_CA_1   5.0 2016-04-22\n",
       "30285  FOODS_3_349_CA_1  13.0 2016-04-23\n",
       "30286  FOODS_3_349_CA_1   4.0 2016-04-24\n",
       "\n",
       "[30287 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_sample.toPandas()\n",
    "train_df.columns = ['unique_id', 'y', 'ds']\n",
    "train_df['ds'] = pd.to_datetime(train_df['ds'])\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6190370e-0cf9-4df8-8ed2-30571b8fa889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLForecast(models=[XGBRegressor, LinearRegression], freq=D, lag_features=['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'expanding_mean_lag1'], date_features=['year', 'month', 'day', 'dayofweek', 'quarter', 'week'], num_threads=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the MLForecast object\n",
    "mlf = MLForecast(\n",
    "    models=[XGBRegressor(), LinearRegression()],  # List of models for forecasting: LightGBM, XGBoost and Linear Regression\n",
    "    freq='D',  # Frequency of the data - 'D' for daily frequency\n",
    "    lags=list(range(1, 7)),  # Specific lags to use as regressors: 1 to 6 days\n",
    "    lag_transforms = {\n",
    "        1:  [expanding_mean],  # Apply expanding mean transformation to the lag of 1 day\n",
    "    },\n",
    "    date_features=['year', 'month', 'day', 'dayofweek', 'quarter', 'week'],  # Date features to use as regressors\n",
    ")\n",
    "\n",
    "mlf.fit(train_df, prediction_intervals=PredictionIntervals(h=horizon, n_windows=2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530a82ac-be6d-427f-ae60-c5534bbaa713",
   "metadata": {},
   "source": [
    "After that, just call `predict` to generate forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e6c65ae-2956-4b63-b86e-03afa60da702",
   "metadata": {},
   "outputs": [],
   "source": [
    "fcst_mlf_df = mlf.predict(28, level=[90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>ALGORITHM</th>\n",
       "      <th>VALUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-25</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>2.144434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>1.840675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>2.049693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>1.887237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>3.703013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          unique_id         ds     ALGORITHM     VALUE\n",
       "0  FOODS_3_020_WI_1 2016-04-25  XGBRegressor  2.144434\n",
       "1  FOODS_3_020_WI_1 2016-04-26  XGBRegressor  1.840675\n",
       "2  FOODS_3_020_WI_1 2016-04-27  XGBRegressor  2.049693\n",
       "3  FOODS_3_020_WI_1 2016-04-28  XGBRegressor  1.887237\n",
       "4  FOODS_3_020_WI_1 2016-04-29  XGBRegressor  3.703013"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melted_df_ml = pd.melt(fcst_mlf_df, id_vars=['unique_id', 'ds'], var_name='ALGORITHM', value_name='VALUE')\n",
    "melted_df_ml.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f66a91aa",
   "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>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>ALGORITHM</th>\n",
       "      <th>VALUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-25</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>2.144434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-26</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>1.840675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>2.049693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-28</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>1.887237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_3_020_WI_1</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>3.703013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>FOODS_3_814_CA_4</td>\n",
       "      <td>2016-05-18</td>\n",
       "      <td>LinearRegression-hi-90</td>\n",
       "      <td>1.762675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>FOODS_3_814_CA_4</td>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>LinearRegression-hi-90</td>\n",
       "      <td>2.327898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>FOODS_3_814_CA_4</td>\n",
       "      <td>2016-05-20</td>\n",
       "      <td>LinearRegression-hi-90</td>\n",
       "      <td>3.146726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>FOODS_3_814_CA_4</td>\n",
       "      <td>2016-05-21</td>\n",
       "      <td>LinearRegression-hi-90</td>\n",
       "      <td>2.298109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>FOODS_3_814_CA_4</td>\n",
       "      <td>2016-05-22</td>\n",
       "      <td>LinearRegression-hi-90</td>\n",
       "      <td>2.784870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3360 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             unique_id         ds               ALGORITHM     VALUE\n",
       "0     FOODS_3_020_WI_1 2016-04-25            XGBRegressor  2.144434\n",
       "1     FOODS_3_020_WI_1 2016-04-26            XGBRegressor  1.840675\n",
       "2     FOODS_3_020_WI_1 2016-04-27            XGBRegressor  2.049693\n",
       "3     FOODS_3_020_WI_1 2016-04-28            XGBRegressor  1.887237\n",
       "4     FOODS_3_020_WI_1 2016-04-29            XGBRegressor  3.703013\n",
       "...                ...        ...                     ...       ...\n",
       "3355  FOODS_3_814_CA_4 2016-05-18  LinearRegression-hi-90  1.762675\n",
       "3356  FOODS_3_814_CA_4 2016-05-19  LinearRegression-hi-90  2.327898\n",
       "3357  FOODS_3_814_CA_4 2016-05-20  LinearRegression-hi-90  3.146726\n",
       "3358  FOODS_3_814_CA_4 2016-05-21  LinearRegression-hi-90  2.298109\n",
       "3359  FOODS_3_814_CA_4 2016-05-22  LinearRegression-hi-90  2.784870\n",
       "\n",
       "[3360 rows x 4 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melted_df_ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "393d4b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "melted_df_ml.columns = melted_df_ml.columns.str.upper()\n",
    "melted_df_ml['DS'] = melted_df_ml['DS'].dt.strftime('%Y-%m-%d')\n",
    "input_df = session.create_dataframe(melted_df_ml)\n",
    "input_df.write.mode('overwrite').save_as_table('TS_M5_forecast_ML')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55cef3a9",
   "metadata": {},
   "source": [
    "Save the forecasts in Snowflake"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b477f2a5",
   "metadata": {},
   "source": [
    "## UDTF for ML Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e7040c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.snowpark.functions import udf, udtf\n",
    "from snowflake.snowpark.types import IntegerType, FloatType, StringType,StructType, StructField\n",
    "import snowflake.snowpark.types as T\n",
    "from snowflake.snowpark.types import PandasDataFrameType, IntegerType, StringType, FloatType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bc3346d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Statement executed successfully.')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sql(\"create or replace warehouse RAJIV with warehouse_size = 'MEDIUM' warehouse_type = 'SNOWPARK-OPTIMIZED'\").collect()\n",
    "session.sql(\"alter warehouse RAJIV set max_concurrency_level = 1\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "00d3d8e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Statement executed successfully.')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sql(\"create or replace warehouse RAJIV with warehouse_size = 'SMALL'\").collect()\n",
    "session.sql(\"alter warehouse RAJIV set max_concurrency_level = 1\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e7f963e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------\n",
      "|\"UNIQUE_ID\"       |\"DS\"                 |\"ALGORITHM\"    |\"VALUE\"               |\n",
      "---------------------------------------------------------------------------------\n",
      "|FOODS_3_170_WI_3  |2016-04-25 00:00:00  |LGBMRegressor  |-0.03366770260341126  |\n",
      "|FOODS_3_170_WI_3  |2016-04-26 00:00:00  |LGBMRegressor  |0.23371279430156836   |\n",
      "|FOODS_3_170_WI_3  |2016-04-27 00:00:00  |LGBMRegressor  |0.3547365122099739    |\n",
      "|FOODS_3_170_WI_3  |2016-04-28 00:00:00  |LGBMRegressor  |-0.01451925628020985  |\n",
      "|FOODS_3_170_WI_3  |2016-04-29 00:00:00  |LGBMRegressor  |-0.1764898359097145   |\n",
      "|FOODS_3_170_WI_3  |2016-04-30 00:00:00  |LGBMRegressor  |0.4227762566571673    |\n",
      "|FOODS_3_170_WI_3  |2016-05-01 00:00:00  |LGBMRegressor  |1.597048630341779     |\n",
      "|FOODS_3_170_WI_3  |2016-05-02 00:00:00  |LGBMRegressor  |0.5978440176258131    |\n",
      "|FOODS_3_170_WI_3  |2016-05-03 00:00:00  |LGBMRegressor  |1.4467681526014018    |\n",
      "|FOODS_3_170_WI_3  |2016-05-04 00:00:00  |LGBMRegressor  |0.6453355593855792    |\n",
      "---------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class forecastml:\n",
    "    def __init__(self)-> None:\n",
    "        pass\n",
    "\n",
    "    def end_partition(self,train_df):\n",
    "        import pandas as pd\n",
    "        from mlforecast import MLForecast\n",
    "        from xgboost import XGBRegressor\n",
    "        from lightgbm import LGBMRegressor\n",
    "        from sklearn.linear_model import LinearRegression\n",
    "        from window_ops.expanding import expanding_mean\n",
    "        from mlforecast.utils import PredictionIntervals\n",
    "    \n",
    "        #train_df['DS'] = pd.to_datetime(train_df['DS'])\n",
    "        train_df.columns = ['unique_id','y','ds'] \n",
    "        \n",
    "        #Train + Forecast Length\n",
    "        horizon = 28\n",
    "\n",
    "        mlf = MLForecast(models=[LGBMRegressor(), XGBRegressor(), LinearRegression()],  # List of models for forecasting: LightGBM, XGBoost and Linear Regression\n",
    "            freq='D',  # Frequency of the data - 'D' for daily frequency\n",
    "            lags=list(range(1, 7)),  # Specific lags to use as regressors: 1 to 6 days\n",
    "            lag_transforms = {\n",
    "                1:  [expanding_mean],  # Apply expanding mean transformation to the lag of 1 day\n",
    "            },\n",
    "            date_features=['year', 'month', 'day', 'dayofweek', 'quarter', 'week'],  # Date features to use as regressors\n",
    "        )\n",
    "\n",
    "        train2 = pd.DataFrame(train_df)\n",
    "        \n",
    "        mlf.fit(train2, prediction_intervals=PredictionIntervals(h=horizon, n_windows=2))\n",
    "        fcst_mlf_df = mlf.predict(28)\n",
    "\n",
    "        #fcst_mlf_df = fcst_mlf_df.reset_index()\n",
    "        melted_df = pd.melt(fcst_mlf_df, id_vars=['unique_id', 'ds'], var_name='ALGORITHM', value_name='VALUE')\n",
    "        melted_df.columns = ['UNIQUE_ID','DS','ALGORITHM','VALUE']\n",
    "\n",
    "        yield melted_df\n",
    "\n",
    "\n",
    "predict_udtf_ml = session.udtf.register(forecastml,is_permanent=False, replace=True\n",
    ", input_types = [T.PandasDataFrameType([T.StringType(),T.FloatType(),T.DateType()])]\n",
    ", output_schema = PandasDataFrameType([StringType(),StringType(),T.StringType(),T.FloatType()],[\"UNIQUE_ID\", \"DS\", \"ALGORITHM\", \"VALUE\"])\n",
    ", packages = ['pandas==1.5.3', 'mlforecast' ,'xgboost', 'scikit-learn','lightgbm'])\n",
    "\n",
    "results = train_sample.select(predict_udtf_ml(\"UNIQUE_ID\",\"Y\",\"DS\").over(partition_by=[\"UNIQUE_ID\"]))\n",
    "#results = df.select(predict_udtf_ml(\"UNIQUE_ID\",\"Y\",\"DS\").over(partition_by=[\"UNIQUE_ID\"]))\n",
    "results.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "97c766bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For 100 series, input was 150k rows, the forecasting table was 25k rows, \n",
    "# ##For all series in one category, input data was 12.5M rows, the forecasting table was 690k rows and took 13 minutes to save on a Medium\n",
    "# for all 30k series, the input data was 50M rows, the forecasting table was  and took 45 minutes 2.6 M rows \n",
    "# This is unoptimized and needs to be optimized\n",
    "results.write.mode('overwrite').save_as_table('TS_M5_forecast_ML_UDTF_temp')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3033f4bb",
   "metadata": {},
   "source": [
    "## Evaluate models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cdbb342",
   "metadata": {},
   "source": [
    "The evaluation is for MAE, MSE, MASE, and SMAPE. The current implementation here does the evaluation in the local environment using numpy/pandas.\n",
    "\n",
    "Let's get the forecasts from Snowflake and evaluate them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "72e1332d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.snowpark.functions import date_trunc\n",
    "forecast_sdf = session.read.table('TS_M5_forecast_ML_UDTF')\n",
    "forecast_sdf1 = session.read.table('TS_M5_forecast_Stats')\n",
    "total = forecast_sdf.union(forecast_sdf1)\n",
    "results = total.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fd257d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "def smape(A, F):\n",
    "    return 100/len(A) * np.sum(2 * np.abs(F - A) / (np.abs(A) + np.abs(F)))\n",
    "\n",
    "def mase(actuals, forecast, seasonality):\n",
    "    # This is a simplified MASE calculation for illustrative purposes\n",
    "    # You may need to adjust the function to match the definition you are using\n",
    "    n = actuals.shape[0]\n",
    "    d = np.abs(np.diff(actuals)).sum()/(n-1)\n",
    "    errors = np.abs(actuals - forecast)\n",
    "    return errors.mean() / d\n",
    "\n",
    "def evaluate_forecasts(group, seasonality):\n",
    "    # Initialize a dictionary to hold evaluation metrics for each unique_id\n",
    "\n",
    "    # Get the unique_id and the actuals for the group\n",
    "    evaluation_metrics = []\n",
    "    actuals = train_df['y'].dropna().values  # Replace 'y' with your actuals column name\n",
    "    \n",
    "    # Ensure there's enough data for the seasonal calculation\n",
    "    if len(actuals) >= seasonality:\n",
    "        unique_id = group['UNIQUE_ID'].iloc[0]\n",
    "        # Get the list of unique algorithms in the group\n",
    "        algorithms = group['ALGORITHM'].unique()\n",
    "        \n",
    "        # Loop through each algorithm's forecast\n",
    "        for algorithm in algorithms:\n",
    "            if algorithm != 'Actual':  # Skip the actuals row\n",
    "                forecast = group[group['ALGORITHM'] == algorithm]['VALUE'].dropna().values\n",
    "                forecast_length = len(forecast)\n",
    "                # Align the actuals and forecast for evaluation\n",
    "                aligned_actuals = actuals[-forecast_length:]\n",
    "                \n",
    "                # Calculate the evaluation metrics\n",
    "                mae_value = mean_absolute_error(aligned_actuals, forecast)\n",
    "                mse_value = mean_squared_error(aligned_actuals, forecast)\n",
    "                smape_value = smape(aligned_actuals, forecast)\n",
    "                mase_value = mase(aligned_actuals, forecast, seasonality)\n",
    "                \n",
    "                # Add the metrics to the dictionary\n",
    "                evaluation_metrics.extend([\n",
    "                    {'UNIQUE_ID': unique_id, 'ALGORITHM': algorithm, 'Metric': 'MAE', 'Value': mae_value},\n",
    "                    {'UNIQUE_ID': unique_id, 'ALGORITHM': algorithm, 'Metric': 'MSE', 'Value': mse_value},\n",
    "                    {'UNIQUE_ID': unique_id, 'ALGORITHM': algorithm, 'Metric': 'SMAPE', 'Value': smape_value},\n",
    "                    {'UNIQUE_ID': unique_id, 'ALGORITHM': algorithm, 'Metric': 'MASE', 'Value': mase_value}\n",
    "                ])\n",
    "    \n",
    "    return pd.DataFrame(evaluation_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "46fb704a",
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_results = results.groupby('UNIQUE_ID').apply(evaluate_forecasts, seasonality=7)\n",
    "final = pd.DataFrame(eval_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c84308a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajishah/anaconda3/envs/working38/lib/python3.8/site-packages/snowflake/snowpark/session.py:1951: UserWarning: Pandas Dataframe has non-standard index of type <class 'pandas.core.indexes.multi.MultiIndex'> which will not be written. Consider changing the index to pd.RangeIndex(start=0,...,step=1) or call reset_index() to keep index as column(s)\n",
      "  success, nchunks, nrows, ci_output = write_pandas(\n"
     ]
    }
   ],
   "source": [
    "final.columns = final.columns.str.upper()\n",
    "input_df = session.create_dataframe(final)\n",
    "input_df.write.mode('overwrite').save_as_table('TS_M5_metrics_series')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f547916",
   "metadata": {},
   "source": [
    "Let's next get aggregated metrics for each algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4ec8f9d9",
   "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>ALGORITHM</th>\n",
       "      <th>METRIC</th>\n",
       "      <th>VALUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADIDA</td>\n",
       "      <td>MAE</td>\n",
       "      <td>7.077116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ADIDA</td>\n",
       "      <td>MASE</td>\n",
       "      <td>1.384653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADIDA</td>\n",
       "      <td>MSE</td>\n",
       "      <td>70.149942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADIDA</td>\n",
       "      <td>SMAPE</td>\n",
       "      <td>127.427623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AutoETS</td>\n",
       "      <td>MAE</td>\n",
       "      <td>7.143749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ALGORITHM METRIC       VALUE\n",
       "0     ADIDA    MAE    7.077116\n",
       "1     ADIDA   MASE    1.384653\n",
       "2     ADIDA    MSE   70.149942\n",
       "3     ADIDA  SMAPE  127.427623\n",
       "4   AutoETS    MAE    7.143749"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'Algorithm' and 'Metric' and calculate the mean for each group\n",
    "overall_performance = eval_results.groupby(['ALGORITHM', 'METRIC'])['VALUE'].mean().reset_index()\n",
    "\n",
    "# Display the overall performance DataFrame\n",
    "overall_performance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "59970dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "overall_performance.columns = overall_performance.columns.str.upper()\n",
    "input_df = session.create_dataframe(overall_performance)\n",
    "input_df.write.mode('overwrite').save_as_table('TS_M5_metrics')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1e5f9e7",
   "metadata": {},
   "source": [
    "## Visualizations of Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d71f762b",
   "metadata": {},
   "outputs": [],
   "source": [
    "overall_performance_df = session.read.table('TS_M5_metrics')\n",
    "overall_performance = overall_performance_df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f41beb74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wh/f1b0hx_54t70j1szv_9_kt0c0000gn/T/ipykernel_98150/1852557930.py:18: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(x=\"ALGORITHM\", y=\"VALUE\", data=metric_data, ci=None)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wh/f1b0hx_54t70j1szv_9_kt0c0000gn/T/ipykernel_98150/1852557930.py:18: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(x=\"ALGORITHM\", y=\"VALUE\", data=metric_data, ci=None)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wh/f1b0hx_54t70j1szv_9_kt0c0000gn/T/ipykernel_98150/1852557930.py:18: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(x=\"ALGORITHM\", y=\"VALUE\", data=metric_data, ci=None)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wh/f1b0hx_54t70j1szv_9_kt0c0000gn/T/ipykernel_98150/1852557930.py:18: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(x=\"ALGORITHM\", y=\"VALUE\", data=metric_data, ci=None)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming overall_performance is the DataFrame with 'Algorithm', 'Metric', and 'Value' columns\n",
    "\n",
    "# Set the aesthetic style of the plots\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Plot a separate bar chart for each metric\n",
    "for metric in overall_performance['METRIC'].unique():\n",
    "    # Filter the DataFrame for the current metric\n",
    "    metric_data = overall_performance[overall_performance['METRIC'] == metric]\n",
    "    \n",
    "    # Initialize the matplotlib figure\n",
    "    plt.figure(figsize=(15, 6))\n",
    "    \n",
    "    # Create a barplot\n",
    "    sns.barplot(x=\"ALGORITHM\", y=\"VALUE\", data=metric_data, ci=None)\n",
    "    \n",
    "    # Add informative title and labels\n",
    "    plt.title(f'Overall Performance for {metric}')\n",
    "    plt.xlabel('Algorithm')\n",
    "    plt.ylabel(f'Average {metric}')\n",
    "    \n",
    "    # Show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "21e116bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "reshaped_df = session.read.table('TS_M5_metrics_series')\n",
    "reshaped_df = reshaped_df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "16f1ed36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set the aesthetic style of the plots\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Loop through each unique metric and create a violin plot\n",
    "for metric in reshaped_df['METRIC'].unique():\n",
    "    plt.figure(figsize=(14, 8))\n",
    "    sns.violinplot(\n",
    "        x='ALGORITHM',\n",
    "        y='VALUE',\n",
    "        data=reshaped_df[reshaped_df['METRIC'] == metric]\n",
    "    )\n",
    "\n",
    "    # Add a title and labels to the plot\n",
    "    plt.title(f'Distribution of {metric} Values for Each Algorithm')\n",
    "    plt.xlabel('Algorithm')\n",
    "    plt.ylabel(metric)\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "68e5b1d0",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2287688742.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[43], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m    Hoping once we get a newer version of Statsforecast, then we can get it working properly\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "## Errors with Statsforecast in creating a UDTF\n",
    "Hoping once we get a newer version of Statsforecast, then we can get it working properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d238f610",
   "metadata": {},
   "outputs": [],
   "source": [
    "class forecast:\n",
    "    def __init__(self)-> None:\n",
    "        pass\n",
    "    \n",
    "    def end_partition(self,train_df):\n",
    "        from statsforecast import StatsForecast\n",
    "        import pandas as pd\n",
    "        from statsforecast.models import (\n",
    "            # SeasonalNaive: A model that uses the previous season's data as the forecast\n",
    "            SeasonalNaive,\n",
    "            # Naive: A simple model that uses the last observed value as the forecast\n",
    "            Naive,\n",
    "            # HistoricAverage: This model uses the average of all historical data as the forecast\n",
    "            HistoricAverage,\n",
    "            # CrostonOptimized: A model specifically designed for intermittent demand forecasting\n",
    "            CrostonOptimized,\n",
    "            # ADIDA: Adaptive combination of Intermittent Demand Approaches, a model designed for intermittent demand\n",
    "            ADIDA,\n",
    "            # IMAPA: Intermittent Multiplicative AutoRegressive Average, a model for intermittent series that incorporates autocorrelation\n",
    "            IMAPA,\n",
    "            # AutoETS: Automated Exponential Smoothing model that automatically selects the best Exponential Smoothing model based on AIC\n",
    "            AutoETS\n",
    "        )\n",
    "        \n",
    "        #train_df['DS'] = pd.to_datetime(train_df['DS'])\n",
    "        train_df.columns = ['unique_id','y','ds'] \n",
    "        \n",
    "        #Train + Forecast Length\n",
    "        horizon = 28\n",
    "\n",
    "        models = [#Naive(), \n",
    "            #SeasonalNaive(season_length=7), \n",
    "            HistoricAverage(),\n",
    "            #CrostonOptimized(),\n",
    "            #ADIDA(),\n",
    "            #IMAPA(),\n",
    "            #AutoETS(season_length=7)\n",
    "            ]\n",
    "\n",
    "        # Instantiate the StatsForecast class\n",
    "        sf = StatsForecast(\n",
    "            models=models,  # A list of models to be used for forecasting\n",
    "            freq='D',  # The frequency of the time series data (in this case, 'D' stands for daily frequency)\n",
    "           # n_jobs=-1,  # The number of CPU cores to use for parallel execution (-1 means use all available cores)\n",
    "        )\n",
    "\n",
    "        train2 = pd.DataFrame(train_df)\n",
    "        print (train2.head())\n",
    "\n",
    "        fcst_df = sf.forecast(df=train2, h=horizon)\n",
    "        print ('Hi')\n",
    "\n",
    "        fcst_df = fcst_df.reset_index()\n",
    "        melted_df = pd.melt(fcst_df, id_vars=['unique_id', 'ds'], var_name='ALGORITHM', value_name='VALUE')\n",
    "        \n",
    "        melted_df.columns = ['UNIQUE_ID','DS','ALGORITHM','VALUE']\n",
    "\n",
    "        print (\"done\")\n",
    "\n",
    "        yield melted_df\n",
    "\n",
    "\n",
    "predict_udtf = session.udtf.register(forecast,is_permanent=False, replace=True\n",
    ", input_types = [T.PandasDataFrameType([T.StringType(),T.FloatType(),T.DateType()])]\n",
    ", output_schema = PandasDataFrameType([StringType(),StringType(),T.StringType(),T.FloatType()],[\"UNIQUE_ID\", \"DS\", \"ALGORITHM\", \"VALUE\"])\n",
    ", packages = ['pandas==2.0.3','statsforecast==1.5.0'])\n",
    "\n",
    "results = train_sample.select(predict_udtf(\"UNIQUE_ID\",\"Y\",\"DS\").over(partition_by=[\"UNIQUE_ID\"]))\n",
    "results.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
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 },
 "nbformat": 4,
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}