{
 "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": "2024-06-12T08:12:22.333464Z",
     "iopub.status.busy": "2024-06-12T08:12:22.333371Z",
     "iopub.status.idle": "2024-06-12T08:12:25.915219Z",
     "shell.execute_reply": "2024-06-12T08:12:25.914955Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m16:12:25\u001b[0m | \u001b[1m\u001b[33;20mWARNING \u001b[0m\u001b[0m | \u001b[1m\u001b[33;20mService endpoint is inaccessible: http://featurebyte-server:8088\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m16:12:25\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;20m16:12:25\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;20m16:12:25\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;20m16:12:25\u001b[0m | \u001b[1m\u001b[33;20mWARNING \u001b[0m\u001b[0m | \u001b[1m\u001b[33;20mRemote SDK version (1.1.0.dev7) is different from local (1.1.0.dev1). Update local SDK to avoid unexpected behavior.\u001b[0m\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20m16:12:25\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;20m16:12:25\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": "2024-06-12T08:12:25.917083Z",
     "iopub.status.busy": "2024-06-12T08:12:25.916930Z",
     "iopub.status.idle": "2024-06-12T08:12:26.347526Z",
     "shell.execute_reply": "2024-06-12T08:12:26.347245Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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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",
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       "      <th>0</th>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                         id                                        name  \\\n",
       "0  666958439767025aff191450  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  2024-06-12T08:12:14.787000  "
      ]
     },
     "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": "2024-06-12T08:12:26.349237Z",
     "iopub.status.busy": "2024-06-12T08:12:26.349133Z",
     "iopub.status.idle": "2024-06-12T08:12:26.809951Z",
     "shell.execute_reply": "2024-06-12T08:12:26.809691Z"
    },
    "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) |████████████████████████████████████████| 9/9 [100%] in 0.2s"
     ]
    },
    {
     "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": "2024-06-12T08:12:26.811701Z",
     "iopub.status.busy": "2024-06-12T08:12:26.811527Z",
     "iopub.status.idle": "2024-06-12T08:12:26.939274Z",
     "shell.execute_reply": "2024-06-12T08:12:26.939015Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <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>66695726850bd33441fdc242</td>\n",
       "      <td>Preview Table with 10 items</td>\n",
       "      <td>view</td>\n",
       "      <td>[10, 2]</td>\n",
       "      <td>playground</td>\n",
       "      <td>2024-06-12T08:07:09.337000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6669570fddd5be620a410f7f</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>2024-06-12T08:06:54.934000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666956fdddd5be620a410f7c</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>2024-06-12T08:06:33.543000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  \\\n",
       "0  66695726850bd33441fdc242   \n",
       "1  6669570fddd5be620a410f7f   \n",
       "2  666956fdddd5be620a410f7c   \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  2024-06-12T08:07:09.337000  \n",
       "1  [1000, 4]         playground  2024-06-12T08:06:54.934000  \n",
       "2  [1000, 3]         playground  2024-06-12T08:06:33.543000  "
      ]
     },
     "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": "2024-06-12T08:12:26.940874Z",
     "iopub.status.busy": "2024-06-12T08:12:26.940763Z",
     "iopub.status.idle": "2024-06-12T08:12:27.013400Z",
     "shell.execute_reply": "2024-06-12T08:12:27.013126Z"
    },
    "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": "2024-06-12T08:12:27.015136Z",
     "iopub.status.busy": "2024-06-12T08:12:27.015035Z",
     "iopub.status.idle": "2024-06-12T08:13:03.740203Z",
     "shell.execute_reply": "2024-06-12T08:13:03.739795Z"
    },
    "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": "2024-06-12T08:13:03.742815Z",
     "iopub.status.busy": "2024-06-12T08:13:03.742614Z",
     "iopub.status.idle": "2024-06-12T08:13:06.037412Z",
     "shell.execute_reply": "2024-06-12T08:13:06.037135Z"
    },
    "tags": []
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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>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",
       "      <th>CUSTOMER_Latest_invoice_Amount</th>\n",
       "      <th>CUSTOMER_Count_of_invoice_14d</th>\n",
       "      <th>CUSTOMER_Avg_of_invoice_Amount_14d</th>\n",
       "      <th>CUSTOMER_Std_of_invoice_Amount_14d</th>\n",
       "      <th>CUSTOMER_Latest_invoice_Amount_Z_Score_to_invoice_Amount_28d</th>\n",
       "      <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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "      <td>80-84</td>\n",
       "      <td>13.13</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.860000</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "      <td>1.0</td>\n",
       "      <td>5.820000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.645410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2653.102500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>1.0</td>\n",
       "      <td>24.790000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.414202</td>\n",
       "      <td>0.624311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>532.296944</td>\n",
       "    </tr>\n",
       "    <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>4.0</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",
       "    </tr>\n",
       "    <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>6.0</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",
       "      <td>50.218611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.724302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994.139722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>cc96d96e-5d02-48dd-b742-d2a0ef633c43</td>\n",
       "      <td>2023-03-07 10:00:46</td>\n",
       "      <td>Laits</td>\n",
       "      <td>2.00</td>\n",
       "      <td>55-59</td>\n",
       "      <td>30.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.655556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>984.909444</td>\n",
       "    </tr>\n",
       "    <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>3.0</td>\n",
       "      <td>8.143333</td>\n",
       "      <td>2.931033</td>\n",
       "      <td>-1.413608</td>\n",
       "      <td>0.541741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1970.083056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>10.0</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>6.0</td>\n",
       "      <td>6.561667</td>\n",
       "      <td>2.902714</td>\n",
       "      <td>-0.493589</td>\n",
       "      <td>0.699924</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1578.428611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "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                            4.0   \n",
       "1                              5.82                            1.0   \n",
       "2                             24.79                            1.0   \n",
       "3                              4.76                            4.0   \n",
       "4                             22.71                            6.0   \n",
       "..                              ...                            ...   \n",
       "995                            3.00                            1.0   \n",
       "996                           30.21                            NaN   \n",
       "997                            4.00                            3.0   \n",
       "998                           17.20                           10.0   \n",
       "999                            5.50                            6.0   \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                                                  NaN   \n",
       "2                                                  NaN   \n",
       "3                                                11.36   \n",
       "4                                                 2.50   \n",
       "..                                                 ...   \n",
       "995                                                NaN   \n",
       "996                                                NaN   \n",
       "997                                                NaN   \n",
       "998                                               4.64   \n",
       "999                                                NaN   \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": "2024-06-12T08:13:06.039360Z",
     "iopub.status.busy": "2024-06-12T08:13:06.039086Z",
     "iopub.status.idle": "2024-06-12T08:13:06.277159Z",
     "shell.execute_reply": "2024-06-12T08:13:06.276873Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>feature_store_name</th>\n",
       "      <th>observation_table_name</th>\n",
       "      <th>shape</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6669586bec7bc32effdab7a4</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",
       "      <td>2024-06-12T08:12:57.839000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  \\\n",
       "0  6669586bec7bc32effdab7a4   \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  2024-06-12T08:12:57.839000  "
      ]
     },
     "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"
   ]
  }
 ],
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