python-recipes/feature-store/00_feast_credit_score.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MCo747t9dL2"
      },
      "source": [
        "![Redis](https://redis.io/wp-content/uploads/2024/04/Logotype.svg?auto=webp&quality=85,75&width=120)\n",
        "\n",
        "# Redis Online Feature Store with Feast\n",
        "\n",
        "In this recipe, we will learn all about [Feature Stores](https://redis.io/solutions/feature-stores/) with **Redis** and **Feast**. This guide is an adaptation of the [Feast Tutorial](https://docs.feast.dev/tutorials/tutorials-overview/real-time-credit-scoring-on-aws) that uses [Redis as the online feature store](https://docs.feast.dev/reference/online-stores/redis).\n",
        "\n",
        "\n",
        "## What are feature stores?\n",
        "A **feature store** architecture makes machine learning systems faster, cheaper, and more reliable.\n",
        "- It centralizes feature definitions so ML teams can reuse work instead of starting from scratch.\n",
        "- It ensures training data and production data stay consistent.\n",
        "- It scales feature serving easily for both real-time and batch (offline) predictions.\n",
        "\n",
        "By reducing errors, wasted time, and technical overhead, a feature store helps teams deliver ML models faster and with less hassle. The typical feature store architecture includes both an **Online** and **Offline** store.\n",
        "\n",
        "![Feature Store](https://raw.githubusercontent.com/redis-developer/redis-ai-resources/main/assets/feature_store.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xXeKcMddDMf_"
      },
      "source": [
        "## Let's Begin!\n",
        "<a href=\"https://colab.research.google.com/github/redis-developer/redis-ai-resources/blob/feat/RAAE-447/feature-store-recipe/python-recipes/feature-store/01_feast_credit_score.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBIoQ08FI_d_"
      },
      "source": [
        "## Environment Setup\n",
        "\n",
        "### Install Python Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pf1wE6aXvofJ",
        "outputId": "cf0247c6-03b0-4314-c389-96867b60fc1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install -q feast['redis']==0.42.0 ipywidgets pandas scikit-learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uEjQ6Z2DH0Nl"
      },
      "source": [
        "### Install Redis Stack\n",
        "\n",
        "In this recipe, **Redis** will be used to store and fetch ML model features through Feast. **We need to make sure we have a Redis instance available.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wojUwDA6H5GH"
      },
      "source": [
        "#### For Colab\n",
        "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly from the Redis package archive."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYmnw8E16UvK",
        "outputId": "db7b19c1-c9d5-45f2-92f0-caf045216234"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb jammy main\n",
            "Starting redis-stack-server, database path /var/lib/redis-stack\n"
          ]
        }
      ],
      "source": [
        "# NBVAL_SKIP\n",
        "%%sh\n",
        "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n",
        "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n",
        "sudo apt-get update  > /dev/null 2>&1\n",
        "sudo apt-get install redis-stack-server  > /dev/null 2>&1\n",
        "redis-stack-server --daemonize yes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdWGcmVeH-Oy"
      },
      "source": [
        "#### For Alternative Environments\n",
        "There are many ways to get the necessary redis-stack instance running\n",
        "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.io/cloud/). Or, if you have your\n",
        "own version of Redis Enterprise running, that works too!\n",
        "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n",
        "3. With docker:\n",
        "\n",
        "    ```bash\n",
        "    docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest\n",
        "    ```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBgAPO0UIFGd"
      },
      "source": [
        "### Define the Redis Connection URL\n",
        "\n",
        "By default this notebook connects to the local instance of Redis Stack. **If you have your own Redis Enterprise instance** - replace `REDIS_PASSWORD`, `REDIS_HOST` and `REDIS_PORT` values with your own."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "vhPBR4sS6We9"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import warnings\n",
        "\n",
        "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\")\n",
        "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\")\n",
        "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\")\n",
        "\n",
        "# Replace values above with your own if using Redis Cloud instance\n",
        "#REDIS_HOST=\"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n",
        "#REDIS_PORT=18374\n",
        "#REDIS_PASSWORD=\"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n",
        "\n",
        "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n",
        "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\"\n",
        "\n",
        "# See https://docs.feast.dev/reference/online-stores/redis for details on Feast connection to Redis\n",
        "REDIS_URL_FEAST = f\"{REDIS_HOST}:{REDIS_PORT},ssl=false,password={REDIS_PASSWORD}\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSEHUJSVIICm"
      },
      "source": [
        "## Load features dataset\n",
        "\n",
        "Below we will make a `creditscore/` directory which will be the home of our Feast repo. We'll create and store additional files there down the road. For now we are loading dataset files into `creditscore/data`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "v15le9QHUDx1"
      },
      "outputs": [],
      "source": [
        "%%sh\n",
        "mkdir creditscore\n",
        "mkdir creditscore/data\n",
        "\n",
        "wget https://redis-ai-resources.s3.us-east-2.amazonaws.com/feature-store/creditscore/credit_history.parquet -q -P creditscore/data\n",
        "wget https://redis-ai-resources.s3.us-east-2.amazonaws.com/feature-store/creditscore/zipcode_table.parquet -q -P creditscore/data\n",
        "wget https://redis-ai-resources.s3.us-east-2.amazonaws.com/feature-store/creditscore/loan_table.parquet -q -P creditscore/data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPN5iDBmFZvU"
      },
      "source": [
        "### Creating feature_store.yaml\n",
        "\n",
        "`feature_store.yaml` is used to configure a feature store with Feast. The file must be located at the root of a feature repository `creditscore/`.\n",
        "\n",
        "See [Redis | Feast Documentation](https://docs.feast.dev/reference/online-stores/redis) for the details of configuring Redis as an online store."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "rpL-6kwUWQbN"
      },
      "outputs": [],
      "source": [
        "feature_store_config = \\\n",
        "f\"\"\"project: creditscore\n",
        "registry: data/registry.db\n",
        "provider: local\n",
        "online_store:\n",
        "    type: redis\n",
        "    connection_string: {REDIS_URL_FEAST}\n",
        "entity_key_serialization_version: 2\n",
        "\"\"\"\n",
        "\n",
        "with open('creditscore/feature_store.yaml', \"w\") as file:\n",
        "    file.write(feature_store_config)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5iIlkheBMAFj",
        "outputId": "e6fbefb8-4661-4b31-dece-53a3c172a491"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "project: creditscore\n",
            "registry: data/registry.db\n",
            "provider: local\n",
            "online_store:\n",
            "    type: redis\n",
            "    connection_string: localhost:6379,ssl=false,password=\n",
            "entity_key_serialization_version: 2\n"
          ]
        }
      ],
      "source": [
        "# Print our feature_store.yaml\n",
        "! cat creditscore/feature_store.yaml"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xbl45OPxGbFY"
      },
      "source": [
        "### Feature Definitions\n",
        "\n",
        "A feature repository can also contain one or more Python files that contain feature definitions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_9xklCQWsQI",
        "outputId": "4ad9db17-c6bd-4406-a724-0a5a73f01733"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing creditscore/features.py\n"
          ]
        }
      ],
      "source": [
        "%%writefile creditscore/features.py\n",
        "\n",
        "from datetime import timedelta\n",
        "\n",
        "from feast import (\n",
        "    Entity,\n",
        "    Field,\n",
        "    FeatureView,\n",
        "    ValueType,\n",
        "    FileSource\n",
        "  )\n",
        "from feast.types import Float32, Int64, String\n",
        "\n",
        "\n",
        "# Feature Definitions\n",
        "\n",
        "## Zipcode Features\n",
        "zipcode = Entity(\n",
        "    name=\"zipcode\",\n",
        "    value_type=ValueType.STRING\n",
        ")\n",
        "zipcode_source = FileSource(\n",
        "    path=\"data/zipcode_table.parquet\",\n",
        "    timestamp_field=\"event_timestamp\",\n",
        "    #event_timestamp_column=\"event_timestamp\",\n",
        "    created_timestamp_column=\"created_timestamp\",\n",
        ")\n",
        "zipcode_features = FeatureView(\n",
        "    name=\"zipcode_features\",\n",
        "    entities=[zipcode],\n",
        "    ttl=timedelta(days=3650),\n",
        "    schema=[\n",
        "        Field(name=\"city\", dtype=String),\n",
        "        Field(name=\"state\", dtype=String),\n",
        "        Field(name=\"location_type\", dtype=String),\n",
        "        Field(name=\"tax_returns_filed\", dtype=Int64),\n",
        "        Field(name=\"population\", dtype=Int64),\n",
        "        Field(name=\"total_wages\", dtype=Int64),\n",
        "    ],\n",
        "    source=zipcode_source,\n",
        ")\n",
        "\n",
        "\n",
        "## Credit History Features\n",
        "dob_ssn = Entity(\n",
        "    name=\"dob_ssn\",\n",
        "    description=\"Date of birth and last four digits of social security number\",\n",
        "    value_type=ValueType.STRING\n",
        ")\n",
        "credit_history_source = FileSource(\n",
        "    path=\"data/credit_history.parquet\",\n",
        "    timestamp_field=\"event_timestamp\",\n",
        "    #event_timestamp_column=\"event_timestamp\",\n",
        "    created_timestamp_column=\"created_timestamp\",\n",
        "\n",
        ")\n",
        "credit_history = FeatureView(\n",
        "    name=\"credit_history\",\n",
        "    entities=[dob_ssn],\n",
        "    ttl=timedelta(days=3650),\n",
        "    schema=[\n",
        "        Field(name=\"dob_ssn\", dtype=String),  # Add entity column for dob_ssn\n",
        "        Field(name=\"credit_card_due\", dtype=Int64),\n",
        "        Field(name=\"mortgage_due\", dtype=Int64),\n",
        "        Field(name=\"student_loan_due\", dtype=Int64),\n",
        "        Field(name=\"vehicle_loan_due\", dtype=Int64),\n",
        "        Field(name=\"hard_pulls\", dtype=Int64),\n",
        "        Field(name=\"missed_payments_2y\", dtype=Int64),\n",
        "        Field(name=\"missed_payments_1y\", dtype=Int64),\n",
        "        Field(name=\"missed_payments_6m\", dtype=Int64),\n",
        "        Field(name=\"bankruptcies\", dtype=Int64),\n",
        "    ],\n",
        "    source=credit_history_source,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Go53F4ZOnkZf"
      },
      "source": [
        "### Create Feast repository"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Ni6sGGjXDks",
        "outputId": "d51d4097-2945-4ab8-ba29-813fff333d00"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/creditscore\n",
            "No project found in the repository. Using project name creditscore defined in feature_store.yaml\n",
            "Applying changes for project creditscore\n",
            "Deploying infrastructure for \u001b[1m\u001b[32mzipcode_features\u001b[0m\n",
            "Deploying infrastructure for \u001b[1m\u001b[32mcredit_history\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "%cd creditscore/\n",
        "!feast apply"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZseuwvTnqbH"
      },
      "source": [
        "### Materialize features into Redis\n",
        "\n",
        "Load data from feature views (parquet files) into the online store (Redis). Use `feast materialize-incremental` to update online store with changes since the last `materialize` call."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PH8wOOLUv75g",
        "outputId": "18cb39e3-e037-4ff5-982c-55d86bfa3b22"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Materializing \u001b[1m\u001b[32m2\u001b[0m feature views to \u001b[1m\u001b[32m2025-01-24 16:57:10+00:00\u001b[0m into the \u001b[1m\u001b[32mredis\u001b[0m online store.\n",
            "\n",
            "\u001b[1m\u001b[32mzipcode_features\u001b[0m from \u001b[1m\u001b[32m2015-01-28 17:19:20+00:00\u001b[0m to \u001b[1m\u001b[32m2025-01-24 16:57:10+00:00\u001b[0m:\n",
            "100%|██████████████████████████████████████████████████████| 28844/28844 [00:02<00:00, 12728.58it/s]\n",
            "\u001b[1m\u001b[32mcredit_history\u001b[0m from \u001b[1m\u001b[32m2015-01-28 17:19:23+00:00\u001b[0m to \u001b[1m\u001b[32m2025-01-24 16:57:10+00:00\u001b[0m:\n",
            "100%|██████████████████████████████████████████████████████| 28633/28633 [00:02<00:00, 10716.44it/s]\n",
            "/content\n"
          ]
        }
      ],
      "source": [
        "warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
        "\n",
        "!feast materialize-incremental 2025-01-24T16:57:10\n",
        "%cd .."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1L8uAaKuoge"
      },
      "source": [
        "## Retreive feature vector from the Redis Online Store\n",
        "\n",
        "`feast apply` and `feast materialize` initialized our feature store, so now we can request features from the Redis online store with `store.get_online_features()` call."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7Yo-3BBgLFy",
        "outputId": "872bb552-bda4-4ccd-f473-6ddee6c692d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'zipcode': ['76104'],\n",
              " 'dob_ssn': ['19630621_4278'],\n",
              " 'total_wages': [142325465],\n",
              " 'state': ['TX'],\n",
              " 'tax_returns_filed': [6058],\n",
              " 'city': ['FORT WORTH'],\n",
              " 'location_type': ['PRIMARY'],\n",
              " 'population': [10534],\n",
              " 'hard_pulls': [1],\n",
              " 'missed_payments_2y': [0],\n",
              " 'bankruptcies': [0],\n",
              " 'missed_payments_6m': [0],\n",
              " 'credit_card_due': [3343],\n",
              " 'student_loan_due': [44375],\n",
              " 'mortgage_due': [378847],\n",
              " 'vehicle_loan_due': [11506],\n",
              " 'missed_payments_1y': [0]}"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from feast import FeatureStore\n",
        "warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
        "\n",
        "\n",
        "store = FeatureStore(repo_path=\"creditscore/\")\n",
        "feast_features = [\n",
        "        \"zipcode_features:city\",\n",
        "        \"zipcode_features:state\",\n",
        "        \"zipcode_features:location_type\",\n",
        "        \"zipcode_features:tax_returns_filed\",\n",
        "        \"zipcode_features:population\",\n",
        "        \"zipcode_features:total_wages\",\n",
        "        \"credit_history:credit_card_due\",\n",
        "        \"credit_history:mortgage_due\",\n",
        "        \"credit_history:student_loan_due\",\n",
        "        \"credit_history:vehicle_loan_due\",\n",
        "        \"credit_history:hard_pulls\",\n",
        "        \"credit_history:missed_payments_2y\",\n",
        "        \"credit_history:missed_payments_1y\",\n",
        "        \"credit_history:missed_payments_6m\",\n",
        "        \"credit_history:bankruptcies\",\n",
        "    ]\n",
        "zipcode = \"76104\"\n",
        "dob_ssn = \"19630621_4278\"\n",
        "\n",
        "feature_vector = store.get_online_features(\n",
        "    features = feast_features,\n",
        "    entity_rows = [{\"zipcode\": zipcode, \"dob_ssn\": dob_ssn}]\n",
        ")\n",
        "feature_vector.to_dict()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tqDuixcKuvYL"
      },
      "source": [
        "## Examine source data\n",
        "\n",
        "`credit_history.parquet` and `zipcode_table.parquet` contains data that would be exposed by our feature store as both online and offline features.  `loan_table.parquet` is used only to train the model and contains historical loan request submissions and target value as approve/deny in `loan_status`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "H2qtjYqQx01b",
        "outputId": "c88c250c-ccfe-4a42-ebf9-41528367589b"
      },
      "outputs": [
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b8e76afb-1920-4481-b8df-899fdf82efc8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   event_timestamp        dob_ssn  credit_card_due  \\\n",
              "0       2020-04-26 18:01:04.746575  19530219_5179             8419   \n",
              "1       2020-04-26 18:01:04.746575  19781116_7723             2944   \n",
              "2       2020-04-26 18:01:04.746575  19931128_5771              833   \n",
              "3       2020-04-26 18:01:04.746575  19500806_6783             5936   \n",
              "4       2020-04-26 18:01:04.746575  19620322_7692             1575   \n",
              "...                            ...            ...              ...   \n",
              "2033293 2021-08-29 18:01:04.746575  19621030_8837             9045   \n",
              "2033294 2021-08-29 18:01:04.746575  19810914_5886             5065   \n",
              "2033295 2021-08-29 18:01:04.746575  19491025_8061              738   \n",
              "2033296 2021-08-29 18:01:04.746575  19751125_4615             3443   \n",
              "2033297 2021-08-29 18:01:04.746575  19960703_3449             1928   \n",
              "\n",
              "         mortgage_due  student_loan_due  vehicle_loan_due  hard_pulls  \\\n",
              "0               91803             22328             15078           0   \n",
              "1              741165              2515             28605           0   \n",
              "2              976522             33000             21733           9   \n",
              "3             1553523             48955             26219           1   \n",
              "4             1067381              9501             15814           1   \n",
              "...               ...               ...               ...         ...   \n",
              "2033293       1106144             25760             13826           8   \n",
              "2033294       1376873             20594             13948           8   \n",
              "2033295        273532             24113             15902          10   \n",
              "2033296       1534792             43133             16294           4   \n",
              "2033297       1197324             24208              4691           1   \n",
              "\n",
              "         missed_payments_2y  missed_payments_1y  missed_payments_6m  \\\n",
              "0                         1                   0                   0   \n",
              "1                         3                   3                   1   \n",
              "2                         7                   0                   0   \n",
              "3                         0                   0                   0   \n",
              "4                         1                   0                   0   \n",
              "...                     ...                 ...                 ...   \n",
              "2033293                   5                   2                   1   \n",
              "2033294                   5                   1                   1   \n",
              "2033295                   1                   2                   1   \n",
              "2033296                   6                   2                   1   \n",
              "2033297                   4                   0                   1   \n",
              "\n",
              "         bankruptcies          created_timestamp  \n",
              "0                   0 2020-04-26 18:01:04.746575  \n",
              "1                   0 2020-04-26 18:01:04.746575  \n",
              "2                   0 2020-04-26 18:01:04.746575  \n",
              "3                   0 2020-04-26 18:01:04.746575  \n",
              "4                   0 2020-04-26 18:01:04.746575  \n",
              "...               ...                        ...  \n",
              "2033293             0 2021-08-29 18:01:04.746575  \n",
              "2033294             0 2021-08-29 18:01:04.746575  \n",
              "2033295             0 2021-08-29 18:01:04.746575  \n",
              "2033296             0 2021-08-29 18:01:04.746575  \n",
              "2033297             0 2021-08-29 18:01:04.746575  \n",
              "\n",
              "[2033298 rows x 12 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "pd.read_parquet(\"creditscore/data/credit_history.parquet\")\n",
        "\n",
        "# zipcode_table.parquet\n",
        "# loan_table.parquet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dvNN9L0wlEdF"
      },
      "source": [
        "## Machine Learning Model Training\n",
        "\n",
        "While our feature store at this point already complete, let's put it to a good use and introduce a `LoadRequestModel` that we will train, using `get_historical_features()` and use to make predictions with `get_online_features()`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "vpclM_myk3g_"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "import feast\n",
        "import joblib\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn import tree\n",
        "from sklearn.exceptions import NotFittedError\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.utils.validation import check_is_fitted\n",
        "warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
        "\n",
        "\n",
        "class LoadRequestModel:\n",
        "    \"\"\"\n",
        "    ML model to classify whether a person should\n",
        "    get approved or rejected for a loan based on a variety of\n",
        "    input factors.\n",
        "    \"\"\"\n",
        "    categorical_features = [\n",
        "        \"person_home_ownership\",\n",
        "        \"loan_intent\",\n",
        "        \"city\",\n",
        "        \"state\",\n",
        "        \"location_type\",\n",
        "    ]\n",
        "\n",
        "    feast_features = [\n",
        "        \"zipcode_features:city\",\n",
        "        \"zipcode_features:state\",\n",
        "        \"zipcode_features:location_type\",\n",
        "        \"zipcode_features:tax_returns_filed\",\n",
        "        \"zipcode_features:population\",\n",
        "        \"zipcode_features:total_wages\",\n",
        "        \"credit_history:credit_card_due\",\n",
        "        \"credit_history:mortgage_due\",\n",
        "        \"credit_history:student_loan_due\",\n",
        "        \"credit_history:vehicle_loan_due\",\n",
        "        \"credit_history:hard_pulls\",\n",
        "        \"credit_history:missed_payments_2y\",\n",
        "        \"credit_history:missed_payments_1y\",\n",
        "        \"credit_history:missed_payments_6m\",\n",
        "        \"credit_history:bankruptcies\",\n",
        "    ]\n",
        "\n",
        "    target = \"loan_status\"\n",
        "    model_filename = \"model.bin\"\n",
        "    encoder_filename = \"encoder.bin\"\n",
        "\n",
        "    def __init__(self,secret=\"\"):\n",
        "        # Load model\n",
        "        if Path(self.model_filename).exists():\n",
        "            self.classifier = joblib.load(self.model_filename)\n",
        "        else:\n",
        "            self.classifier = tree.DecisionTreeClassifier()\n",
        "\n",
        "        # Load ordinal encoder\n",
        "        if Path(self.encoder_filename).exists():\n",
        "            self.encoder = joblib.load(self.encoder_filename)\n",
        "        else:\n",
        "            self.encoder = OrdinalEncoder()\n",
        "\n",
        "        # Set up feature store\n",
        "        self.fs = feast.FeatureStore(repo_path=\"creditscore/\")\n",
        "        #if secret and (\":\" in secret):\n",
        "        #    self.fs.config.online_store.connection_string=secret\n",
        "\n",
        "    def train(self, loans):\n",
        "        train_X, train_Y = self._get_training_features(loans)\n",
        "\n",
        "        self.classifier.fit(train_X[sorted(train_X)], train_Y)\n",
        "        joblib.dump(self.classifier, self.model_filename)\n",
        "\n",
        "    def _get_training_features(self, loans):\n",
        "        training_df = self.fs.get_historical_features(\n",
        "            entity_df=loans, features=self.feast_features\n",
        "        ).to_df()\n",
        "\n",
        "        self._fit_ordinal_encoder(training_df)\n",
        "        self._apply_ordinal_encoding(training_df)\n",
        "        #print(training_df.head())\n",
        "        train_X = training_df[\n",
        "            training_df.columns.drop(self.target)\n",
        "            .drop(\"event_timestamp\")\n",
        "            .drop(\"created_timestamp__\")\n",
        "            .drop(\"loan_id\")\n",
        "            .drop(\"zipcode\")\n",
        "            .drop(\"dob_ssn\")\n",
        "        ]\n",
        "        train_X = train_X.reindex(sorted(train_X.columns), axis=1)\n",
        "        train_Y = training_df.loc[:, self.target]\n",
        "\n",
        "        return train_X, train_Y\n",
        "\n",
        "    def _fit_ordinal_encoder(self, requests):\n",
        "        self.encoder.fit(requests[self.categorical_features])\n",
        "        joblib.dump(self.encoder, self.encoder_filename)\n",
        "\n",
        "    def _apply_ordinal_encoding(self, requests):\n",
        "        requests[self.categorical_features] = self.encoder.transform(\n",
        "            requests[self.categorical_features]\n",
        "        )\n",
        "\n",
        "    def predict(self, request):\n",
        "        # Get online features from Feast\n",
        "        feature_vector = self._get_online_features_from_feast(request)\n",
        "\n",
        "        # Join features to request features\n",
        "        features = request.copy()\n",
        "        features.update(feature_vector)\n",
        "        features_df = pd.DataFrame.from_dict(features)\n",
        "\n",
        "        # Apply ordinal encoding to categorical features\n",
        "        self._apply_ordinal_encoding(features_df)\n",
        "\n",
        "        # Sort columns\n",
        "        features_df = features_df.reindex(sorted(features_df.columns), axis=1)\n",
        "\n",
        "        # Drop unnecessary columns\n",
        "        features_df = features_df[features_df.columns.drop(\"zipcode\").drop(\"dob_ssn\")]\n",
        "\n",
        "        # Make prediction\n",
        "        features_df[\"prediction\"] = self.classifier.predict(features_df)\n",
        "\n",
        "        # return result of credit scoring\n",
        "        return features_df[\"prediction\"].iloc[0]\n",
        "\n",
        "    def _get_online_features_from_feast(self, request):\n",
        "        zipcode = request[\"zipcode\"][0]\n",
        "        dob_ssn = request[\"dob_ssn\"][0]\n",
        "\n",
        "        return self.fs.get_online_features(\n",
        "            entity_rows=[{\"zipcode\": zipcode, \"dob_ssn\": dob_ssn}],\n",
        "            features=self.feast_features,\n",
        "        ).to_dict()\n",
        "\n",
        "    def is_model_trained(self):\n",
        "        try:\n",
        "            check_is_fitted(self.classifier, \"tree_\")\n",
        "        except NotFittedError:\n",
        "            return False\n",
        "        return True\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJMddzepop_-"
      },
      "source": [
        "## Initialize the model\n",
        "\n",
        "Now we need to train the model and make a sample prediction. After training is completed you'll see `model.bin` and `encoder.bin` files in the filesystem."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nw99Ey_0EmZ0",
        "outputId": "ec762747-da85-4f93-cc98-d165b33258e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model not trained. Performing training.\n"
          ]
        }
      ],
      "source": [
        "# Create model\n",
        "model = LoadRequestModel()\n",
        "\n",
        "# Train model (using Parquet for zipcode and credit history features)\n",
        "if not model.is_model_trained():\n",
        "    print(\"Model not trained. Performing training.\")\n",
        "    # Get historic loan data\n",
        "    loans = pd.read_parquet(\"creditscore/data/loan_table.parquet\")\n",
        "    model.train(loans)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mKhKRnCcLrwo"
      },
      "source": [
        "### Make a Loan Request\n",
        "\n",
        "We will now use our trained ML model and feature store to predict whether or not you would get a loan.\n",
        "\n",
        "While making a loan request, make sure that `dob_ssn` and `zipcode` values do exist in the source datasets. You can examine source datasets with `pd.read_parquet(\"creditscore/data/credit_history.parquet\")`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127,
          "referenced_widgets": [
            "d9a931929d2b4eda8790379c157d7060",
            "e4dbf90e7a1943d6b513ebbaea4620fc",
            "70a1a976057f4a7bae49c65b4aed9f4e",
            "be98a638e1de496495281282d3b5afa2",
            "580c994253f6470a8e140f2cc8370328",
            "259feb51fc7a4291b8a0fdeb757dd0af",
            "2a017fb94aed4714b39cdb890a72c364",
            "3c8eebe78d464e4d913a89628fe1c5dd",
            "31603e43689148acad80127c15e2b711"
          ]
        },
        "id": "28yr7TDhlOfa",
        "outputId": "de0b7e4c-26a9-4197-af47-4c05e18bb372"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Select amounts below:\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d9a931929d2b4eda8790379c157d7060",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "IntSlider(value=159000, description='Income: ', max=1000000)"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "be98a638e1de496495281282d3b5afa2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "IntSlider(value=5000, description='Loan Amount: ', max=1000000)"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2a017fb94aed4714b39cdb890a72c364",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "IntSlider(value=16, description='Interest Rate: ', max=90, min=1)"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import ipywidgets as widgets\n",
        "\n",
        "# initialize loan request with sample data\n",
        "loan_request = {\n",
        "    \"zipcode\": [76104],\n",
        "    \"dob_ssn\": [\"19630621_4278\"],\n",
        "    \"person_age\": [63],\n",
        "    \"person_income\": [159000],\n",
        "    \"person_home_ownership\": [\"RENT\"],\n",
        "    \"person_emp_length\": [123.0],\n",
        "    \"loan_intent\": [\"PERSONAL\"],\n",
        "    \"loan_amnt\": [5000],\n",
        "    \"loan_int_rate\": [16.02],\n",
        "}\n",
        "\n",
        "\n",
        "slider_income = widgets.IntSlider(loan_request[\"person_income\"][0], max=1000000, min=0, description=\"Income: \")\n",
        "slider_amount = widgets.IntSlider(loan_request[\"loan_amnt\"][0], max=1000000, min=0, description=\"Loan Amount: \")\n",
        "slider_int_rate = widgets.IntSlider(loan_request[\"loan_int_rate\"][0], max=90, min=1, description=\"Interest Rate: \")\n",
        "\n",
        "print(\"Select amounts below:\")\n",
        "display(slider_income, slider_amount, slider_int_rate)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yFhF1XpWPbTG",
        "outputId": "dc18d87b-548e-4097-ce63-d76d97dca85d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loan rejected!\n"
          ]
        }
      ],
      "source": [
        "loan_request[\"person_income\"] = [slider_income.value]\n",
        "loan_request[\"loan_amnt\"] = [slider_amount.value]\n",
        "loan_request[\"loan_int_rate\"] = [slider_int_rate.value]\n",
        "\n",
        "\n",
        "# Make online prediction (using Redis for retrieving online features)\n",
        "result = model.predict(loan_request)\n",
        "\n",
        "if result == 0:\n",
        "    print(\"Loan approved!\")\n",
        "elif result == 1:\n",
        "    print(\"Loan rejected!\")\n",
        "\n",
        "warnings.simplefilter(\"ignore\", DeprecationWarning)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dvWW46znVObR"
      },
      "source": [
        "Let's inspect an individual loan request payload."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "MI6ggOO1pH65",
        "outputId": "92368c70-3244-4d91-9bef-c246e81d7c85"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"loan_request_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          5000,\n          \"19630621_4278\",\n          123.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-b72960bc-2cdb-4ede-9d43-fed6955a294a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>zipcode</th>\n",
              "      <td>76104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dob_ssn</th>\n",
              "      <td>19630621_4278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>person_age</th>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>person_income</th>\n",
              "      <td>159000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>person_home_ownership</th>\n",
              "      <td>RENT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>person_emp_length</th>\n",
              "      <td>123.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_intent</th>\n",
              "      <td>PERSONAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_amnt</th>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_int_rate</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b72960bc-2cdb-4ede-9d43-fed6955a294a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b72960bc-2cdb-4ede-9d43-fed6955a294a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-60e766a6-cd0e-4684-b25f-0cd72f871d00\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60e766a6-cd0e-4684-b25f-0cd72f871d00')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-60e766a6-cd0e-4684-b25f-0cd72f871d00 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                   0\n",
              "zipcode                        76104\n",
              "dob_ssn                19630621_4278\n",
              "person_age                        63\n",
              "person_income                 159000\n",
              "person_home_ownership           RENT\n",
              "person_emp_length              123.0\n",
              "loan_intent                 PERSONAL\n",
              "loan_amnt                       5000\n",
              "loan_int_rate                     16"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
        "\n",
        "loan_request_df = pd.DataFrame.from_dict(loan_request)\n",
        "loan_request_df.transpose()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOUteI-NVVKG"
      },
      "source": [
        "Let's inspect the feature store features pulled from Redis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "fg6ZFHF7uMr2",
        "outputId": "c230bc1a-2cdb-485a-bb52-b6cf7974524e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"feature_vector_df\",\n  \"rows\": 17,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          0,\n          44375,\n          \"76104\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>zipcode</th>\n",
              "      <td>76104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dob_ssn</th>\n",
              "      <td>19630621_4278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_wages</th>\n",
              "      <td>142325465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>TX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tax_returns_filed</th>\n",
              "      <td>6058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city</th>\n",
              "      <td>FORT WORTH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>location_type</th>\n",
              "      <td>PRIMARY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population</th>\n",
              "      <td>10534</td>\n",
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              "    <tr>\n",
              "      <th>hard_pulls</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>missed_payments_2y</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bankruptcies</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>missed_payments_6m</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_card_due</th>\n",
              "      <td>3343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>student_loan_due</th>\n",
              "      <td>44375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mortgage_due</th>\n",
              "      <td>378847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vehicle_loan_due</th>\n",
              "      <td>11506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>missed_payments_1y</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e1e1740-a459-45ac-9014-0a401468e3b3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5e1e1740-a459-45ac-9014-0a401468e3b3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5e1e1740-a459-45ac-9014-0a401468e3b3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e9d18245-7871-4329-89f3-a01ea6db9326\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9d18245-7871-4329-89f3-a01ea6db9326')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e9d18245-7871-4329-89f3-a01ea6db9326 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                0\n",
              "zipcode                     76104\n",
              "dob_ssn             19630621_4278\n",
              "total_wages             142325465\n",
              "state                          TX\n",
              "tax_returns_filed            6058\n",
              "city                   FORT WORTH\n",
              "location_type             PRIMARY\n",
              "population                  10534\n",
              "hard_pulls                      1\n",
              "missed_payments_2y              0\n",
              "bankruptcies                    0\n",
              "missed_payments_6m              0\n",
              "credit_card_due              3343\n",
              "student_loan_due            44375\n",
              "mortgage_due               378847\n",
              "vehicle_loan_due            11506\n",
              "missed_payments_1y              0"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
        "\n",
        "feature_vector = model._get_online_features_from_feast(loan_request)\n",
        "feature_vector_df=pd.DataFrame.from_dict(feature_vector)\n",
        "feature_vector_df.transpose()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-ZxTMF9VuX_"
      },
      "source": [
        "Join the features to see the entire input sent to the credit prediction model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "RbTQfP0ytpKm",
        "outputId": "bf08bcd3-3085-4330-e23b-54d0f86c0c28"
      },
      "outputs": [
        {
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              "    (() => {\n",
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              "        document.querySelector('#df-5fd25bf4-549b-4142-aeb6-d18230fdc1f2 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "text/plain": [
              "                                   0\n",
              "zipcode                        76104\n",
              "dob_ssn                19630621_4278\n",
              "person_age                        63\n",
              "person_income                 159000\n",
              "person_home_ownership           RENT\n",
              "person_emp_length              123.0\n",
              "loan_intent                 PERSONAL\n",
              "loan_amnt                       5000\n",
              "loan_int_rate                     16\n",
              "total_wages                142325465\n",
              "state                             TX\n",
              "tax_returns_filed               6058\n",
              "city                      FORT WORTH\n",
              "location_type                PRIMARY\n",
              "population                     10534\n",
              "hard_pulls                         1\n",
              "missed_payments_2y                 0\n",
              "bankruptcies                       0\n",
              "missed_payments_6m                 0\n",
              "credit_card_due                 3343\n",
              "student_loan_due               44375\n",
              "mortgage_due                  378847\n",
              "vehicle_loan_due               11506\n",
              "missed_payments_1y                 0"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "features = loan_request.copy()\n",
        "features.update(feature_vector)\n",
        "features_df = pd.DataFrame.from_dict(features)\n",
        "features_df.transpose()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fiV0HSXNw4MA",
        "outputId": "71bff765-0448-4242-c931-45d8a6446faa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loan rejected!\n"
          ]
        }
      ],
      "source": [
        "result = model.predict(loan_request)\n",
        "\n",
        "if result == 0:\n",
        "    print(\"Loan approved!\")\n",
        "elif result == 1:\n",
        "    print(\"Loan rejected!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9LUGgDZIN7l"
      },
      "source": [
        "## Benchmarking\n",
        "\n",
        "The key advantage of Redis as a Online feature store is it's ability to very quickly retreive features on request. Below, we'll retreive the same data from Online store (Redis) and from the Offline store (parquet) and measure execution time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "E0yBnM3VV09H"
      },
      "outputs": [],
      "source": [
        "store = FeatureStore(repo_path=\"creditscore/\")\n",
        "feast_features = [\n",
        "        \"zipcode_features:city\",\n",
        "        \"zipcode_features:state\",\n",
        "        \"zipcode_features:location_type\",\n",
        "        \"zipcode_features:tax_returns_filed\",\n",
        "        \"zipcode_features:population\",\n",
        "        \"zipcode_features:total_wages\",\n",
        "        \"credit_history:credit_card_due\",\n",
        "        \"credit_history:mortgage_due\",\n",
        "        \"credit_history:student_loan_due\",\n",
        "        \"credit_history:vehicle_loan_due\",\n",
        "        \"credit_history:hard_pulls\",\n",
        "        \"credit_history:missed_payments_2y\",\n",
        "        \"credit_history:missed_payments_1y\",\n",
        "        \"credit_history:missed_payments_6m\",\n",
        "        \"credit_history:bankruptcies\",\n",
        "    ]\n",
        "zipcode = \"76104\"\n",
        "dob_ssn = \"19630621_4278\"\n",
        "\n",
        "entity_rows=[{\"zipcode\": zipcode, \"dob_ssn\": dob_ssn}]\n",
        "entity_rows_df=pd.DataFrame(entity_rows)\n",
        "entity_rows_df[\"event_timestamp\"]=pd.to_datetime(\"2020-04-26 18:01:04.746575\")\n",
        "entity_rows_df['zipcode'] = entity_rows_df['zipcode'].astype(int)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EwZW_IdvWPMx"
      },
      "source": [
        "Online feature store retrieval benchmark:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khcx8l4WWFST",
        "outputId": "29280860-9f59-41d7-9ee2-df094c69eaf0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "18.3 ms ± 4.24 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
          ]
        }
      ],
      "source": [
        "%%timeit\n",
        "\n",
        "online_features = store.get_online_features(\n",
        "    features = feast_features,\n",
        "    entity_rows = entity_rows\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stN6hs52WRWo"
      },
      "source": [
        "Offline feature store retrieval benchmark:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMXzXeJxWTxI",
        "outputId": "f6856e54-5bf5-4caf-8681-de6c37372e47"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4.06 s ± 874 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
          ]
        }
      ],
      "source": [
        "%%timeit\n",
        "\n",
        "offline_features= store.get_historical_features(\n",
        "    entity_df = entity_rows_df,\n",
        "    features = feast_features\n",
        ").to_df()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RvUwM40W86O"
      },
      "source": [
        ">Note: That's more than a 100x difference. (typically)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vGFAJh58D7I",
        "outputId": "c3fc6d99-7f6f-403c-f0d0-054c1815862d"
      },
      "outputs": [
        {
          "data": {
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              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019701123_4498creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019591114_4878creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019960329_8318creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019921122_6221creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0099737creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019921028_8664creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x04\\x00\\x00\\x001082creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x04\\x00\\x00\\x004929creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019480901_7786creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019660219_4269creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019461118_2114creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019611127_9426creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0022967creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019800827_7446creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019490418_9512creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0054903creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019760306_3330creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0022656creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019590612_3618creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019560919_2757creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0049436creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019710220_5280creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019920612_1273creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0078333creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019490112_9394creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0059601creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019950628_7285creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0031030creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0020010125_3481creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0035228creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019741109_5016creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0026032creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0089118creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0040601creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0075217creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0076550creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0020010325_9396creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019730507_8742creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019560128_1008creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0032164creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019700113_2978creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019560415_1712creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0056221creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019630929_6832creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019460324_1049creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019640825_3358creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0038167creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0047106creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019550316_4272creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x04\\x00\\x00\\x002643creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019991228_7925creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019740609_4875creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019820924_1126creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0042303creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019871226_1226creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019590818_8005creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0051466creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0020000319_6455creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0093584creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019911204_8105creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0086403creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019790323_7676creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0092342creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0071742creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0089043creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0046774creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0047807creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0091042creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019530105_9941creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019800121_4411creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0038668creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0030006creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019990224_2613creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019690403_1557creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0061319creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019610703_1330creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0093608creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0023609creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0047244creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019990503_5880creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0081141creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0068818creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019800413_2593creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019880505_1690creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0091203creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019500511_7145creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019670203_8936creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0032060creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019831003_7385creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019651104_2938creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019790704_4149creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0059802creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0013865creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0011377creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0016827creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0016201creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019510305_7529creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019600707_2475creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019510225_8060creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019710511_1417creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x04\\x00\\x00\\x005069creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0027559creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019571111_4225creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0035051creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0061802creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019500919_5275creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019600215_7725creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0019009creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019500124_5400creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0066202creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0058335creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019930811_1809creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019480210_6097creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019700515_8374creditscore',\n",
              " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019550528_9708creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0084711creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0071082creditscore',\n",
              " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0027964creditscore',\n",
              " ...]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# retreive sample of keys from redis\n",
        "from redis import Redis\n",
        "\n",
        "redis_client = Redis.from_url(REDIS_URL)\n",
        "redis_client.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yaoQYnpgEPzm"
      },
      "source": [
        "### Cleanup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "EKqGsgQGEIDP"
      },
      "outputs": [],
      "source": [
        "%cd creditscore/\n",
        "!feast teardown\n",
        "%cd .."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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      },
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