{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e8be874",
   "metadata": {},
   "source": [
    "### Compute historical feature values\n",
    "\n",
    "Historical feature values are needed to train and test Machine Learning models. \n",
    "\n",
    "Let's take the feature list we just created and compute feature values for a given observation table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6475ae1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T07:35:01.466507Z",
     "iopub.status.busy": "2024-04-26T07:35:01.465955Z",
     "iopub.status.idle": "2024-04-26T07:35:03.963406Z",
     "shell.execute_reply": "2024-04-26T07:35:03.962588Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m15:35:03\u001b[0m | \u001b[1m\u001b[38;20mINFO    \u001b[0m\u001b[0m | \u001b[1m\u001b[38;20mSDK version: 1.0.2.dev46\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m15:35:03\u001b[0m | \u001b[1m\u001b[38;20mINFO    \u001b[0m\u001b[0m | \u001b[1m\u001b[38;20mNo catalog activated.\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m15:35:03\u001b[0m | \u001b[1m\u001b[38;20mINFO    \u001b[0m\u001b[0m | \u001b[1m\u001b[38;20mUsing profile: tutorial\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m15:35:03\u001b[0m | \u001b[1m\u001b[38;20mINFO    \u001b[0m\u001b[0m | \u001b[1m\u001b[38;20mUsing configuration file at: /Users/gxav/.featurebyte/config.yaml\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m15:35:03\u001b[0m | \u001b[1m\u001b[38;20mINFO    \u001b[0m\u001b[0m | \u001b[1m\u001b[38;20mActive profile: tutorial (https://tutorials.featurebyte.com/api/v1)\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m15:35:03\u001b[0m | \u001b[1m\u001b[38;20mINFO    \u001b[0m\u001b[0m | \u001b[1m\u001b[38;20mSDK version: 1.0.2.dev46\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m15:35:03\u001b[0m | \u001b[1m\u001b[38;20mINFO    \u001b[0m\u001b[0m | \u001b[1m\u001b[38;20mNo catalog activated.\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m15:35:03\u001b[0m | \u001b[1m\u001b[38;20mINFO    \u001b[0m\u001b[0m | \u001b[1m\u001b[38;20mCatalog activated: Grocery Dataset Tutorial\u001b[0m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import featurebyte as fb\n",
    "\n",
    "# Set your profile to the tutorial environment\n",
    "fb.use_profile(\"tutorial\")\n",
    "\n",
    "catalog_name = \"Grocery Dataset Tutorial\"\n",
    "catalog = fb.Catalog.activate(catalog_name)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3085ea",
   "metadata": {},
   "source": [
    "#### List feature lists in Catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25d0339a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T07:35:03.968307Z",
     "iopub.status.busy": "2024-04-26T07:35:03.967717Z",
     "iopub.status.idle": "2024-04-26T07:35:05.164683Z",
     "shell.execute_reply": "2024-04-26T07:35:05.164198Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>num_feature</th>\n",
       "      <th>status</th>\n",
       "      <th>deployed</th>\n",
       "      <th>readiness_frac</th>\n",
       "      <th>online_frac</th>\n",
       "      <th>tables</th>\n",
       "      <th>entities</th>\n",
       "      <th>primary_entity</th>\n",
       "      <th>created_at</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>662b59023bbb17418af6e696</td>\n",
       "      <td>Customer x ProductGroup Simple FeatureList</td>\n",
       "      <td>9</td>\n",
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       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[GROCERYCUSTOMER, GROCERYINVOICE, INVOICEITEMS...</td>\n",
       "      <td>[customer, productgroup]</td>\n",
       "      <td>[customer, productgroup]</td>\n",
       "      <td>2024-04-26T07:34:53.267000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id                                        name  \\\n",
       "0  662b59023bbb17418af6e696  Customer x ProductGroup Simple FeatureList   \n",
       "\n",
       "   num_feature status  deployed  readiness_frac  online_frac  \\\n",
       "0            9  DRAFT     False             0.0          0.0   \n",
       "\n",
       "                                              tables  \\\n",
       "0  [GROCERYCUSTOMER, GROCERYINVOICE, INVOICEITEMS...   \n",
       "\n",
       "                   entities            primary_entity  \\\n",
       "0  [customer, productgroup]  [customer, productgroup]   \n",
       "\n",
       "                   created_at  \n",
       "0  2024-04-26T07:34:53.267000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catalog.list_feature_lists()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe93856",
   "metadata": {},
   "source": [
    "#### Get Feature List from Catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96147a09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T07:35:05.167893Z",
     "iopub.status.busy": "2024-04-26T07:35:05.167684Z",
     "iopub.status.idle": "2024-04-26T07:35:06.694716Z",
     "shell.execute_reply": "2024-04-26T07:35:06.694235Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading Feature(s) |                                        | ▁▃▅ 0/9 [0%] in 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading Feature(s) |                                        | ▂▄▆ 0/9 [0%] in 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading Feature(s) |                                        | ▃▅▇ 0/9 [0%] in 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading Feature(s) |                                        | ▄▆█ 0/9 [0%] in 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading Feature(s) |                                        | ▅▇▇ 0/9 [0%] in 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading Feature(s) |                                        | ▆█▆ 0/9 [0%] in 1s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading Feature(s) |████████████████████████████████████████| 9/9 [100%] in 0.6s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "simple_feature_list = catalog.get_feature_list(\"Customer x ProductGroup Simple FeatureList\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "282901eb",
   "metadata": {},
   "source": [
    "#### Get an observation table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02737afa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T07:35:06.697920Z",
     "iopub.status.busy": "2024-04-26T07:35:06.697562Z",
     "iopub.status.idle": "2024-04-26T07:35:07.140729Z",
     "shell.execute_reply": "2024-04-26T07:35:07.140081Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>shape</th>\n",
       "      <th>feature_store_name</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>662b57ec0afca1c7b079f598</td>\n",
       "      <td>Preview Table with 10 items</td>\n",
       "      <td>view</td>\n",
       "      <td>[10, 2]</td>\n",
       "      <td>playground</td>\n",
       "      <td>2024-04-26T07:29:55.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>662b57d8daab72d046ad966e</td>\n",
       "      <td>In_Store_Customer_x_ProductGroup_Spending_next...</td>\n",
       "      <td>observation_table</td>\n",
       "      <td>[1000, 4]</td>\n",
       "      <td>playground</td>\n",
       "      <td>2024-04-26T07:29:38.704000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>662b57c572b2fff854399a7a</td>\n",
       "      <td>In_Store_Customer_x_ProductGroup_2023_1K</td>\n",
       "      <td>uploaded_file</td>\n",
       "      <td>[1000, 3]</td>\n",
       "      <td>playground</td>\n",
       "      <td>2024-04-26T07:29:18.860000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  \\\n",
       "0  662b57ec0afca1c7b079f598   \n",
       "1  662b57d8daab72d046ad966e   \n",
       "2  662b57c572b2fff854399a7a   \n",
       "\n",
       "                                                name               type  \\\n",
       "0                        Preview Table with 10 items               view   \n",
       "1  In_Store_Customer_x_ProductGroup_Spending_next...  observation_table   \n",
       "2           In_Store_Customer_x_ProductGroup_2023_1K      uploaded_file   \n",
       "\n",
       "       shape feature_store_name                  created_at  \n",
       "0    [10, 2]         playground  2024-04-26T07:29:55.010000  \n",
       "1  [1000, 4]         playground  2024-04-26T07:29:38.704000  \n",
       "2  [1000, 3]         playground  2024-04-26T07:29:18.860000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List observation tables\n",
    "catalog.list_observation_tables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6cc65546",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T07:35:07.145770Z",
     "iopub.status.busy": "2024-04-26T07:35:07.145359Z",
     "iopub.status.idle": "2024-04-26T07:35:07.226791Z",
     "shell.execute_reply": "2024-04-26T07:35:07.225878Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get observation table: 'In_Store_Customer_x_ProductGroup_Spending_next_2_weeks_2023_1K'\n",
    "training_observations = catalog.get_observation_table(\n",
    "    \"In_Store_Customer_x_ProductGroup_Spending_next_2_weeks_2023_1K\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d536db06",
   "metadata": {},
   "source": [
    "#### Compute historical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c070ac56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T07:35:07.233553Z",
     "iopub.status.busy": "2024-04-26T07:35:07.233114Z",
     "iopub.status.idle": "2024-04-26T07:35:46.030277Z",
     "shell.execute_reply": "2024-04-26T07:35:46.028537Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Working... |                                        | ▁▃▅ 0% in 0s (~0s, 0.0%/s)"
     ]
    },
    {
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   ],
   "source": [
    "# Create historical feature table\n",
    "table_name =\\\n",
    "\"Simple Training Simple Training for In_Store_Customer_x_ProductGroup_Spending_next_2_weeks_2023_1K\"\n",
    "training_data_table = simple_feature_list.compute_historical_feature_table(\n",
    "    training_observations,\n",
    "    historical_feature_table_name=table_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e616521d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T07:35:46.038968Z",
     "iopub.status.busy": "2024-04-26T07:35:46.038502Z",
     "iopub.status.idle": "2024-04-26T07:35:49.070529Z",
     "shell.execute_reply": "2024-04-26T07:35:49.070003Z"
    },
    "tags": []
   },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Downloading table |███████████████████████████████████     | ▃▅▇ 875/1000 [88%] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Downloading table |████████████████████████████████████████| 1000/1000 [100%] in"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GROCERYCUSTOMERGUID</th>\n",
       "      <th>POINT_IN_TIME</th>\n",
       "      <th>PRODUCTGROUP</th>\n",
       "      <th>CUSTOMER_x_PRODUCTGROUP_Sum_of_TotalCost_next_2_weeks</th>\n",
       "      <th>CUSTOMER_Age_band</th>\n",
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       "      <th>CUSTOMER_vs_OVERALL_item_TotalCost_across_product_ProductGroups_26w</th>\n",
       "      <th>CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d</th>\n",
       "      <th>CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>699efd7f-aba2-4515-9335-2c8040a94f9f</td>\n",
       "      <td>2023-12-11 08:51:22</td>\n",
       "      <td>Fromages</td>\n",
       "      <td>14.18</td>\n",
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       "      <td>4</td>\n",
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       "      <td>3.720329</td>\n",
       "      <td>0.179880</td>\n",
       "      <td>0.683171</td>\n",
       "      <td>6.00</td>\n",
       "      <td>166.499167</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>125dfe7d-eac0-4eab-94d8-1cd008e1641c</td>\n",
       "      <td>2023-05-16 09:00:11</td>\n",
       "      <td>Laits</td>\n",
       "      <td>1.85</td>\n",
       "      <td>30-34</td>\n",
       "      <td>5.82</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.645410</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>2</th>\n",
       "      <td>326b6ccb-0891-49fe-acbf-31d06c6d9e67</td>\n",
       "      <td>2023-03-20 13:34:55</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35-39</td>\n",
       "      <td>24.79</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.414202</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e42fa5f3-7737-4c6a-9ef4-856f113e60bd</td>\n",
       "      <td>2023-12-18 19:04:45</td>\n",
       "      <td>Fromages</td>\n",
       "      <td>9.00</td>\n",
       "      <td>25-29</td>\n",
       "      <td>4.76</td>\n",
       "      <td>4</td>\n",
       "      <td>12.860000</td>\n",
       "      <td>7.439772</td>\n",
       "      <td>-0.637723</td>\n",
       "      <td>0.649094</td>\n",
       "      <td>11.36</td>\n",
       "      <td>241.682222</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dde029d7-ceca-4e44-aad0-38e22ba11b74</td>\n",
       "      <td>2023-09-08 15:00:07</td>\n",
       "      <td>Pains</td>\n",
       "      <td>3.49</td>\n",
       "      <td>40-44</td>\n",
       "      <td>22.71</td>\n",
       "      <td>6</td>\n",
       "      <td>10.605000</td>\n",
       "      <td>8.144472</td>\n",
       "      <td>0.930581</td>\n",
       "      <td>0.740797</td>\n",
       "      <td>2.50</td>\n",
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       "      <th>995</th>\n",
       "      <td>e883912f-82c4-4ca8-bfa9-0bdeb46dd4c5</td>\n",
       "      <td>2023-06-28 20:16:15</td>\n",
       "      <td>Céréales</td>\n",
       "      <td>0.00</td>\n",
       "      <td>70-74</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.724302</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>2023-03-07 10:00:46</td>\n",
       "      <td>Laits</td>\n",
       "      <td>2.00</td>\n",
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       "      <td>30.21</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.655556</td>\n",
       "      <td>0.00</td>\n",
       "      <td>984.909444</td>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1b82b9eb-cc54-4cc4-a7e3-9a7417faa8a5</td>\n",
       "      <td>2023-11-16 20:44:02</td>\n",
       "      <td>Laits</td>\n",
       "      <td>2.69</td>\n",
       "      <td>40-44</td>\n",
       "      <td>4.00</td>\n",
       "      <td>3</td>\n",
       "      <td>8.143333</td>\n",
       "      <td>2.931033</td>\n",
       "      <td>-1.413608</td>\n",
       "      <td>0.541741</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>998</th>\n",
       "      <td>c0ca0bda-e7f5-4748-9b14-0e7ba9a07a47</td>\n",
       "      <td>2023-04-06 14:58:43</td>\n",
       "      <td>Laits</td>\n",
       "      <td>2.32</td>\n",
       "      <td>65-69</td>\n",
       "      <td>17.20</td>\n",
       "      <td>10</td>\n",
       "      <td>20.122000</td>\n",
       "      <td>13.270356</td>\n",
       "      <td>-0.079666</td>\n",
       "      <td>0.808877</td>\n",
       "      <td>4.64</td>\n",
       "      <td>241.992500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>a0588833-ba78-41a4-b36a-d36bcd68e27e</td>\n",
       "      <td>2023-09-16 13:40:19</td>\n",
       "      <td>Fromages</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20-24</td>\n",
       "      <td>5.50</td>\n",
       "      <td>6</td>\n",
       "      <td>6.561667</td>\n",
       "      <td>2.902714</td>\n",
       "      <td>-0.493589</td>\n",
       "      <td>0.699924</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1578.428611</td>\n",
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      "text/plain": [
       "                      GROCERYCUSTOMERGUID       POINT_IN_TIME PRODUCTGROUP  \\\n",
       "0    699efd7f-aba2-4515-9335-2c8040a94f9f 2023-12-11 08:51:22     Fromages   \n",
       "1    125dfe7d-eac0-4eab-94d8-1cd008e1641c 2023-05-16 09:00:11        Laits   \n",
       "2    326b6ccb-0891-49fe-acbf-31d06c6d9e67 2023-03-20 13:34:55     Céréales   \n",
       "3    e42fa5f3-7737-4c6a-9ef4-856f113e60bd 2023-12-18 19:04:45     Fromages   \n",
       "4    dde029d7-ceca-4e44-aad0-38e22ba11b74 2023-09-08 15:00:07        Pains   \n",
       "..                                    ...                 ...          ...   \n",
       "995  e883912f-82c4-4ca8-bfa9-0bdeb46dd4c5 2023-06-28 20:16:15     Céréales   \n",
       "996  cc96d96e-5d02-48dd-b742-d2a0ef633c43 2023-03-07 10:00:46        Laits   \n",
       "997  1b82b9eb-cc54-4cc4-a7e3-9a7417faa8a5 2023-11-16 20:44:02        Laits   \n",
       "998  c0ca0bda-e7f5-4748-9b14-0e7ba9a07a47 2023-04-06 14:58:43        Laits   \n",
       "999  a0588833-ba78-41a4-b36a-d36bcd68e27e 2023-09-16 13:40:19     Fromages   \n",
       "\n",
       "     CUSTOMER_x_PRODUCTGROUP_Sum_of_TotalCost_next_2_weeks CUSTOMER_Age_band  \\\n",
       "0                                                14.18                 80-84   \n",
       "1                                                 1.85                 30-34   \n",
       "2                                                 0.00                 35-39   \n",
       "3                                                 9.00                 25-29   \n",
       "4                                                 3.49                 40-44   \n",
       "..                                                 ...                   ...   \n",
       "995                                               0.00                 70-74   \n",
       "996                                               2.00                 55-59   \n",
       "997                                               2.69                 40-44   \n",
       "998                                               2.32                 65-69   \n",
       "999                                               0.00                 20-24   \n",
       "\n",
       "     CUSTOMER_Latest_invoice_Amount  CUSTOMER_Count_of_invoice_14d  \\\n",
       "0                             13.13                              4   \n",
       "1                              5.82                              1   \n",
       "2                             24.79                              1   \n",
       "3                              4.76                              4   \n",
       "4                             22.71                              6   \n",
       "..                              ...                            ...   \n",
       "995                            3.00                              1   \n",
       "996                           30.21                              0   \n",
       "997                            4.00                              3   \n",
       "998                           17.20                             10   \n",
       "999                            5.50                              6   \n",
       "\n",
       "     CUSTOMER_Avg_of_invoice_Amount_14d  CUSTOMER_Std_of_invoice_Amount_14d  \\\n",
       "0                             13.860000                            3.720329   \n",
       "1                              5.820000                            0.000000   \n",
       "2                             24.790000                            0.000000   \n",
       "3                             12.860000                            7.439772   \n",
       "4                             10.605000                            8.144472   \n",
       "..                                  ...                                 ...   \n",
       "995                            3.000000                            0.000000   \n",
       "996                                 NaN                                 NaN   \n",
       "997                            8.143333                            2.931033   \n",
       "998                           20.122000                           13.270356   \n",
       "999                            6.561667                            2.902714   \n",
       "\n",
       "     CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d  \\\n",
       "0                                             0.179880              \n",
       "1                                            -1.000000              \n",
       "2                                             1.414202              \n",
       "3                                            -0.637723              \n",
       "4                                             0.930581              \n",
       "..                                                 ...              \n",
       "995                                                NaN              \n",
       "996                                                NaN              \n",
       "997                                          -1.413608              \n",
       "998                                          -0.079666              \n",
       "999                                          -0.493589              \n",
       "\n",
       "     CUSTOMER_vs_OVERALL_item_TotalCost_across_product_ProductGroups_26w  \\\n",
       "0                                             0.683171                     \n",
       "1                                             0.645410                     \n",
       "2                                             0.624311                     \n",
       "3                                             0.649094                     \n",
       "4                                             0.740797                     \n",
       "..                                                 ...                     \n",
       "995                                           0.724302                     \n",
       "996                                           0.655556                     \n",
       "997                                           0.541741                     \n",
       "998                                           0.808877                     \n",
       "999                                           0.699924                     \n",
       "\n",
       "     CUSTOMER_x_PRODUCTGROUP_Sum_of_item_TotalCost_14d  \\\n",
       "0                                                 6.00   \n",
       "1                                                 0.00   \n",
       "2                                                 0.00   \n",
       "3                                                11.36   \n",
       "4                                                 2.50   \n",
       "..                                                 ...   \n",
       "995                                               0.00   \n",
       "996                                               0.00   \n",
       "997                                               0.00   \n",
       "998                                               4.64   \n",
       "999                                               0.00   \n",
       "\n",
       "     CUSTOMER_x_PRODUCTGROUP_Time_Since_Latest_Timestamp  \n",
       "0                                           166.499167    \n",
       "1                                          2653.102500    \n",
       "2                                           532.296944    \n",
       "3                                           241.682222    \n",
       "4                                            50.218611    \n",
       "..                                                 ...    \n",
       "995                                        1994.139722    \n",
       "996                                         984.909444    \n",
       "997                                        1970.083056    \n",
       "998                                         241.992500    \n",
       "999                                        1578.428611    \n",
       "\n",
       "[1000 rows x 13 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(training_data_table.to_pandas())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a5faa69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T07:35:49.073511Z",
     "iopub.status.busy": "2024-04-26T07:35:49.073256Z",
     "iopub.status.idle": "2024-04-26T07:35:49.705200Z",
     "shell.execute_reply": "2024-04-26T07:35:49.704289Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>feature_store_name</th>\n",
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       "      <th>shape</th>\n",
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       "      <th>0</th>\n",
       "      <td>662b592bab51a900e2d8e938</td>\n",
       "      <td>Simple Training Simple Training for In_Store_C...</td>\n",
       "      <td>playground</td>\n",
       "      <td>In_Store_Customer_x_ProductGroup_Spending_next...</td>\n",
       "      <td>[1000, 13]</td>\n",
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      "text/plain": [
       "                         id  \\\n",
       "0  662b592bab51a900e2d8e938   \n",
       "\n",
       "                                                name feature_store_name  \\\n",
       "0  Simple Training Simple Training for In_Store_C...         playground   \n",
       "\n",
       "                              observation_table_name       shape  \\\n",
       "0  In_Store_Customer_x_ProductGroup_Spending_next...  [1000, 13]   \n",
       "\n",
       "                   created_at  \n",
       "0  2024-04-26T07:35:40.835000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### List historical feature tables from catalog\n",
    "catalog.list_historical_feature_tables()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a48de9bb",
   "metadata": {},
   "source": [
    "### Concepts in this tutorial\n",
    "- [More on Historical feature serving](https://docs.featurebyte.com/latest/about/glossary/#historical-feature-serving)\n",
    "\n",
    "#### SDK reference for\n",
    "- [Historical feature table](https://docs.featurebyte.com/latest/reference/core/historical_feature_table/)\n",
    "- [FeatureList.compute historical feature table()](https://docs.featurebyte.com/latest/reference/featurebyte.api.feature_list.FeatureList.compute_historical_feature_table/)\n",
    "- [FeatureList.compute_historical_features()](https://docs.featurebyte.com/latest/reference/featurebyte.api.feature_list.FeatureList.compute_historical_features/) to compute directly a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39690066",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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