{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a596ae7f-73b5-4674-85a8-011a971e4f66",
   "metadata": {},
   "source": [
    "# Running Inference on Legacy (Non-SageWorks) Models\n",
    "This notebook goes through the steps of setting up and running inference on Models that weren't generated in SageWorks.\n",
    "\n",
    "```\n",
    "model = Model(\"the old model\")\n",
    "model.onboard()\n",
    "```\n",
    "Answer all the Model onboarding questions, the tricky one is 'features' (note: capitalization matters)\n",
    "\n",
    "```\n",
    "end = Endpoint(\"the old endpoint\")\n",
    "end.onboard()\n",
    "```\n",
    "\n",
    "Answer all the onboard questions, if you're asked for input put the Model Name.\n",
    "\n",
    "After this is done, you'll need follow this notebook for 'remapping' the evaluation data into a form that the old model can use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43bbee7d-709e-42b5-b818-934fe5be755a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Set the environment variable\n",
    "os.environ['SAGEWORKS_CONFIG'] = '/Users/briford/.sageworks/scp_sandbox.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "314a562d-776b-404a-8c4f-1ea56078d2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab the features from the model (this will be set during onboard())\n",
    "from sageworks.api.model import Model\n",
    "model = Model(\"LogS-pH7-Class-0-231025\")\n",
    "target = model.target()\n",
    "print(target)\n",
    "features = model.features()\n",
    "print(f\"{features[:5]}...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "026249ac-18bd-4041-b73b-87e01a764450",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab the evaluation data\n",
    "from sageworks.api.feature_set import FeatureSet\n",
    "fs = FeatureSet(\"solubility_featurized_fs\")\n",
    "training_view = fs.view(\"training\").table\n",
    "eval_data = fs.query(f\"select * from {training_view} where training=0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "429c64b8-3e0c-4de4-9dee-9f02ec4cac85",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_columns = eval_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88cad15d-31c4-4089-aa56-c0613278021d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_map(data_columns, features):\n",
    "    map = dict()\n",
    "    for f in features:\n",
    "        if f.lower() in data_columns:\n",
    "            map[f.lower()] = f\n",
    "    return map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10d6a542-0cef-4d89-b275-2325604178ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "map = build_map(data_columns, features)\n",
    "map[\"class\"] = target  # Add the target variable name change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5d613d8-8e5c-4cdd-9edc-e10fa81f6c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the feature and target columns to 'match' what the Legacy Model expects\n",
    "eval_data.rename(columns=map, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8a55517-71d4-458e-a486-0f6365d43823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab the Legacy Endpoint\n",
    "from sageworks.api.endpoint import Endpoint\n",
    "end = Endpoint(\"LogS-pH7-Class-0-231025\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57788b7b-42ef-487a-abba-f0ae35188133",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run inference on the Legacy Endpoint\n",
    "end.inference(eval_data, capture_uuid=\"2024_03_19_holdout\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b840f3b0-fabd-45b1-8d44-fcd188f8e242",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This might not be strictly necssary but it will make sure the details are up to date\n",
    "model.details(recompute=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}