{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Custom Tasks\n",
    "\n",
    "The following provides an example workflow of creating a blueprint with a pre-existing\n",
    "custom task.\n",
    "\n",
    "Please note that for the time being, custom tasks should be created through\n",
    "the [DataRobot Python Client](https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.27.0/reference/modeling/spec/custom_task.html)\n",
    "or via the UI."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "from datarobot_bp_workshop import Workshop\n",
    "\n",
    "w = Workshop()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "id": "9176bf4c-1783-4bc3-87d4-6915e4e59291",
   "metadata": {},
   "source": [
    "## Find Custom Tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "28714a43-b35d-4b15-b066-ccb12ef415b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Awesome Model: [CUSTOMR_6019ae978cc598a46199cee1] \n",
       "  - This is the best model ever."
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.search_tasks('awesome task')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "69492172-09db-4985-baae-f5fc04477ced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Awesome Model: [CUSTOMR_6019ae978cc598a46199cee1] \n",
       "  - This is the best model ever."
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "97663da6-681e-4f39-93f9-bd1467c57a6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Awesome Model (CUSTOMR_6019ae978cc598a46199cee1)\n",
       "\n",
       "Input Summary: Numeric Data\n",
       "Output Method: TaskOutputMethod.PREDICT\n",
       "\n",
       "Task Parameters:\n",
       "  version_id (version_id) = latest_6019ae978cc598a46199cee1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1(w.TaskInputs.NUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2fd47145-9eca-4876-91ab-42a54579f4de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Awesome Model (CUSTOMR_6019ae978cc598a46199cee1)\n",
       "\n",
       "Input Summary: (None)\n",
       "Output Method: TaskOutputMethod.PREDICT\n",
       "\n",
       "Task Parameters:\n",
       "  version_id (version_id) = latest_6019ae978cc598a46199cee1"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.CustomTask('CUSTOMR_6019ae978cc598a46199cee1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9fbcab2f-2cf8-4423-ab03-8d0dd9e4dd67",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Latest (latest_6019ae978cc598a46199cee1): str\n",
       "\n",
       "v3.0 (6019e2418311cc8207a5f8e1): str\n",
       "\n",
       "v2.10 (6019dff0509159ede309f9c9): str\n",
       "\n",
       "v2.9 (6019dc3b8311cc8207a5f7d9): str\n",
       "\n",
       "v2.8 (6019dbcb4f6322a6283883d9): str\n",
       "\n",
       "v2.7 (6019db4d041c71bd7ea1c670): str\n",
       "\n",
       "v2.6 (6019da5d4f6322a628388364): str\n",
       "\n",
       "v2.5 (6019d924be257008648e3c62): str\n",
       "\n",
       "v2.4 (6019d7db3d7d080b078e3c39): str\n",
       "\n",
       "v2.3 (6019d744356f3c430b38828d): str\n",
       "\n",
       "v2.2 (6019d305be257008648e3c0c): str\n",
       "\n",
       "v2.1 (6019d2e045e619fc03a2eead): str\n",
       "\n",
       "v2.0 (6019d2bd3d7d080b078e3b66): str\n",
       "\n",
       "v1.3 (6019cf0735270cbe238e3c76): str\n",
       "\n",
       "v1.2 (6019b9fdbf5b0a42aba1c6e9): str\n",
       "\n",
       "v1.1 (6019b81729ae9ab5ad8e3c26): str\n",
       "\n",
       "v1.0 (6019afe4dcd97e1e5ebfee13): str"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1.versions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e263d92b-6c0f-45a7-bf89-96081882028f",
   "metadata": {},
   "source": [
    "## Build a blueprint with a custom task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8ed5b209-5401-4df4-9df5-660221eebaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pni = w.Tasks.PNI2(w.TaskInputs.NUM)\n",
    "rdt = w.Tasks.RDT5(pni)\n",
    "binning = w.Tasks.BINNING(pni)\n",
    "customr = w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1(rdt, binning)\n",
    "custom_bp = w.BlueprintGraph(customr, name='My Fun Custom Blueprint').save()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c287e072-24e2-442d-9ecb-f4039b10ab5d",
   "metadata": {},
   "source": [
    "## Set custom task version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e20cd7d4-8e1b-488b-83d2-5383001b7ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "customr.version = w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1.versions.v2_7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f09b97b3-6d0d-4a51-9e76-5cd9abdc66ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Awesome Model (CUSTOMR_6019ae978cc598a46199cee1)\n",
       "\n",
       "Input Summary: Smooth Ridit Transform (RDT5) | Binning of numerical variables (BINNING)\n",
       "Output Method: TaskOutputMethod.PREDICT\n",
       "\n",
       "Task Parameters:\n",
       "  version_id (version_id) = 6019db4d041c71bd7ea1c670"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d7d71399-d9ee-4013-bd52-af9faef26e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "customr.version = w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1.versions.Latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2ae48e48-b5c3-4c4d-84d1-43b79fa9da72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name: 'My Fun Custom Blueprint'\n",
       "\n",
       "Input Data: Numeric\n",
       "Tasks: Missing Values Imputed | Smooth Ridit Transform | Binning of numerical variables | Awesome Model"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custom_bp.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2240b29b-58de-45e2-b61a-e97070a7acfb",
   "metadata": {},
   "source": [
    "### Find, View, and Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5ad30a7c-8a8d-4006-b1a9-73fdaf553735",
   "metadata": {},
   "outputs": [],
   "source": [
    "bps = list(w.list(limit=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "50c58a9e-42b9-4bd5-aacc-72fa3d913f62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bps[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9ebe1423-263b-4ed2-b2e7-4ba0c9d0f270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name: 'My Fun Custom Blueprint'\n",
       "\n",
       "Input Data: Numeric\n",
       "Tasks: Missing Values Imputed | Smooth Ridit Transform | Binning of numerical variables | Awesome Model"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custom_bp.train(project_id=project_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3939c4cb-dde3-49f2-9d6f-97e76e9cc6e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[32mBlueprint deleted.\u001B[0m\n"
     ]
    }
   ],
   "source": [
    "custom_bp.delete()"
   ]
  }
 ],
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