Colab - DRUM Wild Mode.ipynb

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      "name": "Unstructured Model Predict - Return Shap Values and Predictions.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/timsetsfire/odsc-ml-drum/blob/main/Colab%20-%20DRUM%20Wild%20Mode.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "-2tZB_o-pDmP"
      },
      "source": [
        "# DRUM Wild Mode\n",
        "\n",
        "DRUM Wild Mode, formally known as Unstructured Mode, allows user to to pass and return almost anything.  No validation of data occurs and it is your responsibility to verify correctness.\n",
        "\n",
        "In this example, we will host an LGMB Regressor and for every request, we will return predictions and shap values for said predictions.  \n"
      ]
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone https://github.com/timsetsfire/odsc-ml-drum.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'odsc-ml-drum'...\n",
            "remote: Enumerating objects: 244, done.\u001b[K\n",
            "remote: Counting objects: 100% (244/244), done.\u001b[K\n",
            "remote: Compressing objects: 100% (210/210), done.\u001b[K\n",
            "remote: Total 416 (delta 106), reused 108 (delta 32), pack-reused 172\u001b[K\n",
            "Receiving objects: 100% (416/416), 77.77 MiB | 33.50 MiB/s, done.\n",
            "Resolving deltas: 100% (173/173), done.\n"
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          "name": "stdout"
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "! pip install -r /content/odsc-ml-drum/src/other_models/unstructured_predict/requirements.txt -q"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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            "\u001b[?25h  Building wheel for PyYAML (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[31mERROR: tensorflow 2.3.0 has requirement numpy<1.19.0,>=1.16.0, but you'll have numpy 1.19.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement scipy==1.4.1, but you'll have scipy 1.5.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n"
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        "colab": {
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      "source": [
        "!pip install datarobot-drum"
      ],
      "execution_count": null,
      "outputs": [
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            "Building wheels for collected packages: strictyaml, progress, argparse-formatter, memory-profiler, uwsgi\n",
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            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: ruamel.yaml.clib, ruamel.yaml, strictyaml, progress, uwsgi, flask-cors, py4j, mlpiper, texttable, requests-toolbelt, trafaret, attrs, datarobot, argparse-formatter, importlib-metadata, argcomplete, websocket-client, docker, memory-profiler, datarobot-drum\n",
            "  Found existing installation: attrs 20.2.0\n",
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            "Successfully installed argcomplete-1.11.1 argparse-formatter-1.2 attrs-19.3.0 datarobot-2.21.3 datarobot-drum-1.4.2 docker-4.3.1 flask-cors-3.0.9 importlib-metadata-1.7.0 memory-profiler-0.58.0 mlpiper-2.4.1 progress-1.5 py4j-0.10.9.1 requests-toolbelt-0.9.1 ruamel.yaml-0.16.12 ruamel.yaml.clib-0.2.2 strictyaml-1.0.6 texttable-1.6.3 trafaret-1.2.0 uwsgi-2.0.19.1 websocket-client-0.57.0\n"
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    {
      "cell_type": "code",
      "metadata": {
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      },
      "source": [
        "start_server = ['drum',\n",
        " 'server',\n",
        " '--code-dir',\n",
        " '/content/odsc-ml-drum/src/other_models/unstructured_predict',\n",
        " '--target-type',\n",
        " 'unstructured',\n",
        " '--address',\n",
        " 'localhost:6789',\n",
        " '--verbose']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79gzPtE3oarC"
      },
      "source": [
        "import io\n",
        "import requests\n",
        "import avro.io\n",
        "import avro\n",
        "from avro.datafile import DataFileReader\n",
        "import subprocess\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbkLOr8fpZrX"
      },
      "source": [
        "server = subprocess.Popen(start_server, stdout=subprocess.PIPE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "source": [
        "requests.request(\"GET\", \"http://localhost:6789/\").content"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b'{\"message\":\"OK\"}\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "NJJU6xGloarV"
      },
      "source": [
        "csv_in_bytes = open(\"/content/odsc-ml-drum/data/loss_cost_short.csv\", \"rb\").read()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6z-r5hBEoarY"
      },
      "source": [
        "import requests\n",
        "\n",
        "url = \"http://localhost:6789/predictUnstructured/?ret_mode=binary\"\n",
        "\n",
        "payload = csv_in_bytes\n",
        "headers = {\n",
        "  'Content-Type': 'application/octet-stream'\n",
        "}\n",
        "\n",
        "response = requests.request(\"POST\", url, headers=headers, data = payload)\n",
        "\n",
        "return_bytes = response.content"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ml2ySInoara",
        "outputId": "20cf8998-7bd5-4411-f398-9d5af57357d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "schema = avro.schema.parse(open(\"/content/odsc-ml-drum/src/other_models/unstructured_predict/schema.avsc\", \"rb\").read())\n",
        "bytes_reader = io.BytesIO(return_bytes)\n",
        "datum_reader = avro.io.DatumReader(schema)\n",
        "returned_data = DataFileReader(bytes_reader, avro.io.DatumReader())\n",
        "predictions = []  \n",
        "for pred in returned_data:\n",
        "    temp = pred[\"shap_values\"]\n",
        "    temp[\"prediction\"] = pred[\"prediction\"]\n",
        "    predictions.append(temp)\n",
        "pd.DataFrame(predictions).iloc[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SHAP_CustomerTenure                    -0.009229\n",
              "SHAP_DriverAge                         -0.067060\n",
              "SHAP_EngineCapacity                    -0.126928\n",
              "SHAP_NumberOfDrivers                   -0.210533\n",
              "SHAP_VehicleAge                        -0.002556\n",
              "SHAP_VehicleCostNew                    -0.007075\n",
              "SHAP_Zipcode_Aged_18_24                -0.021351\n",
              "SHAP_Zipcode_Aged_25_29                -0.006317\n",
              "SHAP_Zipcode_Aged_30_39                 0.009671\n",
              "SHAP_Zipcode_Aged_40_44                -0.003087\n",
              "SHAP_Zipcode_Aged_45_49                -0.286157\n",
              "SHAP_Zipcode_Aged_50_59                -0.050674\n",
              "SHAP_Zipcode_Aged_60                    0.100407\n",
              "SHAP_Zipcode_CommuteViaCar              0.497439\n",
              "SHAP_Zipcode_PersonsPerHousehold        0.001143\n",
              "SHAP_Zipcode_VehiclesPerHousehold       0.030764\n",
              "SHAP_Zipcode_annualMileage             -0.006449\n",
              "SHAP_DistributionChannel               -0.008335\n",
              "SHAP_VehicleModel                       0.068915\n",
              "SHAP_Zipcode                           -0.001565\n",
              "SHAP_VehicleMake                       -0.009019\n",
              "SHAP_ClientType                         0.000041\n",
              "SHAP_MaritalStatus                     -0.174615\n",
              "prediction                           2493.327881\n",
              "Name: 0, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    }
  ]
}