06_Test_Best_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Best Model\n",
    "In this notebook, we recover into Python the best trained model and evaluate it on the test data.\n",
    "\n",
    "The steps in this notebook are\n",
    "- [import libraries](#import),\n",
    "- [read in the Azure ML workspace](#workspace),\n",
    "- [download the best trained model](#download), and\n",
    "- [evaluate the result using the test data](#evaluate)\n",
    "\n",
    "## Imports  <a id='import'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from azureml.core import Workspace\n",
    "from azureml.core.model import Model\n",
    "import azureml.core\n",
    "from get_auth import get_auth\n",
    "print('azureml.core.VERSION={}'.format(azureml.core.VERSION))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in the Azure ML workspace  <a id='workspace'></a>\n",
    "Read in the the workspace created in a previous notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "auth = get_auth()\n",
    "ws = Workspace.from_config(auth=auth)\n",
    "ws_details = ws.get_details()\n",
    "print('Name:\\t\\t{}\\nLocation:\\t{}'\n",
    "      .format(ws_details['name'],\n",
    "              ws_details['location']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the best trained model  <a id='download'></a>\n",
    "Get the best trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"FAQ_ranker\"\n",
    "model = Model(ws, name=model_name)\n",
    "print(model.name, model.version, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.download(target_dir=\"outputs\", exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model using the test data <a id='evaluate'></a>\n",
    "Look at the model's performance on the held-aside test data. This can take a couple of minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -t scripts/TestClassifier.py --model $model_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This completes the hyperparameter tuning tutorial. You may be interested in the [MLAKSDeployAML tutorial](https://github.com/Microsoft/MLAKSDeployAML) for an example of deploying a trained Python model for scoring at scale.\n",
    "\n",
    "The [next notebook](07_Train_With_AML_Pipeline.ipynb) shows how to create an Azure Machine Learning pipeline that can be called to do the work of notebooks [04_Hyperparameter_Random_Search](04_Hyperparameter_Random_Search.ipynb) and [05_Train_Best_Model](05_Train_Best_Model.ipynb) of this tutorial."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}