hyperparameter_tuning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning using HyperDrive\n",
    "\n",
    "TODO: Import Dependencies. In the cell below, import all the dependencies that you will need to complete the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "gather": {
     "logged": 1598531914256
    }
   },
   "outputs": [],
   "source": [
    "from azureml.core import Workspace, Experiment\n",
    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
    "from azureml.widgets import RunDetails\n",
    "from azureml.train.sklearn import SKLearn\n",
    "from azureml.train.hyperdrive.run import PrimaryMetricGoal\n",
    "from azureml.train.hyperdrive.policy import BanditPolicy\n",
    "from azureml.train.hyperdrive.sampling import RandomParameterSampling\n",
    "from azureml.train.hyperdrive.runconfig import HyperDriveConfig\n",
    "from azureml.train.hyperdrive.parameter_expressions import choice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "gather": {
     "logged": 1598531917374
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "experiment_name = 'CapstoneExp'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optCompute exists already\n",
      "Succeeded\n",
      "AmlCompute wait for completion finished\n",
      "\n",
      "Minimum number of nodes requested have been provisioned\n"
     ]
    }
   ],
   "source": [
    "# Compute name should contain only letters, digits, hyphen and should be 2-16 charachters long\n",
    "compute_name = \"optCompute\"\n",
    "try:\n",
    "    trainCluster = ComputeTarget(ws, compute_name)\n",
    "    print(f\"{compute_name} exists already\")\n",
    "except:\n",
    "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"Standard_D2_V2\", max_nodes=5)\n",
    "    trainCluster = ComputeTarget.create(ws, compute_name, compute_config)\n",
    "trainCluster.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gather": {
     "logged": 1598531923519
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "source": [
    "## Hyperdrive Configuration\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "gather": {
     "logged": 1598544893076
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'SKLearn' estimator is deprecated. Please use 'ScriptRunConfig' from 'azureml.core.script_run_config' with your own defined environment or the AzureML-Tutorial curated environment.\n"
     ]
    }
   ],
   "source": [
    "# TODO: Create an early termination policy. This is not required if you are using Bayesian sampling.\n",
    "early_termination_policy = BanditPolicy(evaluation_interval=1, slack_factor=0.2, delay_evaluation=5)\n",
    "\n",
    "#TODO: Create the different params that you will be using during training\n",
    "param_sampling = RandomParameterSampling({\"--C\": choice(1,2,3,4,5), \"--max_iter\": choice(80,100,120,150,170,200)})\n",
    "\n",
    "#TODO: Create your estimator and hyperdrive config\n",
    "estimator = SKLearn(source_directory='./training', compute_target=trainCluster, entry_script='train.py')\n",
    "\n",
    "hyperdrive_run_config = HyperDriveConfig(estimator=estimator, policy=early_termination_policy, primary_metric_name=\"Accuracy\",\n",
    "                                          hyperparameter_sampling=param_sampling,\n",
    "                                         max_total_runs=20,\n",
    "                                          primary_metric_goal=PrimaryMetricGoal.MAXIMIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "gather": {
     "logged": 1598544897941
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:If 'script' has been provided here and a script file name has been specified in 'run_config', 'script' provided in ScriptRunConfig initialization will take precedence.\n"
     ]
    }
   ],
   "source": [
    "#TODO: Submit your experiment\n",
    "hyperDrive_run = experiment.submit(hyperdrive_run_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "gather": {
     "logged": 1598544898497
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "source": [
    "## Run Details\n",
    "\n",
    "TODO: In the cell below, use the `RunDetails` widget to show the different experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "gather": {
     "logged": 1598546648408
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f53bc7c39424bc09eabff41319fc7df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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space\\r\\n[2021-01-09T13:16:43.614902][GENERATOR][INFO]Successfully sampled '20' jobs, they will soon be submitted to the execution target.\\r\\n[2021-01-09T13:16:45.2953115Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2021-01-09T13:17:14.857625][GENERATOR][INFO]Max number of jobs '20' reached for experiment.\\r\\n[2021-01-09T13:17:14.990172][GENERATOR][INFO]All jobs generated.\\r\\n[2021-01-09T13:17:16.1805510Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_0'\\r\\n[2021-01-09T13:17:16.1797329Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2021-01-09T13:17:16.2077610Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_4'\\r\\n[2021-01-09T13:17:16.2044444Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_3'\\r\\n[2021-01-09T13:17:16.1872755Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_1'\\r\\n[2021-01-09T13:17:16.2165321Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_5'\\r\\n[2021-01-09T13:17:16.2293319Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_6'\\r\\n[2021-01-09T13:17:16.3387577Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_9'\\r\\n[2021-01-09T13:17:16.1877806Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_2'\\r\\n[2021-01-09T13:17:16.2408455Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_7'\\r\\n[2021-01-09T13:17:16.3651825Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_11'\\r\\n[2021-01-09T13:17:16.2190701Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_19'\\r\\n[2021-01-09T13:17:16.3992338Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_12'\\r\\n[2021-01-09T13:17:16.2953664Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_8'\\r\\n[2021-01-09T13:17:16.3509644Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_10'\\r\\n[2021-01-09T13:17:16.4317085Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_14'\\r\\n[2021-01-09T13:17:16.4942111Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_17'\\r\\n[2021-01-09T13:17:16.4788215Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_16'\\r\\n[2021-01-09T13:17:16.4172771Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_13'\\r\\n[2021-01-09T13:17:16.4487859Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_15'\\r\\n[2021-01-09T13:17:16.2915432Z][SCHEDULER][INFO]Scheduling job, id='HD_1fadb825-c230-40c3-8751-c6332a21be96_18'\\r\\n[2021-01-09T13:17:16.8894971Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_1fadb825-c230-40c3-8751-c6332a21be96_18'\\r\\n[2021-01-09T13:17:16.9554843Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_1fadb825-c230-40c3-8751-c6332a21be96_10'\\r\\n[2021-01-09T13:17:16.9718477Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_1fadb825-c230-40c3-8751-c6332a21be96_0'\\r\\n[2021-01-09T13:17:17.1057098Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_1fadb825-c230-40c3-8751-c6332a21be96_2'\\r\\n[2021-01-09T13:17:17.1441320Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_1fadb825-c230-40c3-8751-c6332a21be96_5'\\r\\n[2021-01-09T13:17:17.1523450Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_1fadb825-c230-40c3-8751-c6332a21be96_4'\\r\\n[2021-01-09T13:17:17.2469696Z][SCHEDULER][INFO]Successfully scheduled a job. Id='HD_1fadb825-c230-40c3-8751-c6332a21be96_6'\\r\\n[2021-01-09T13:17:17.2548563Z][SCHEDULER][INFO]Successfully scheduled a job. 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https://southcentralus.experiments.azureml.net/subscriptions/2c48c51c-bd47-40d4-abbe-fb8eabd19c8c/resourceGroups/aml-quickstarts-134086/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134086/experiments/**SCRUBBED**/runs/HD_1fadb825-c230-40c3-8751-c6332a21be96_19] do not contain any metrics with the primary metric name at this moment, policy cannot be applied.\\r\\n[2021-01-09T13:24:41.723571][ENFORCER][INFO]Jobs [https://southcentralus.experiments.azureml.net/subscriptions/2c48c51c-bd47-40d4-abbe-fb8eabd19c8c/resourceGroups/aml-quickstarts-134086/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-134086/experiments/**SCRUBBED**/runs/HD_1fadb825-c230-40c3-8751-c6332a21be96_1, 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RunDetails(hyperDrive_run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Model\n",
    "\n",
    "TODO: In the cell below, get the best model from the hyperdrive experiments and display all the properties of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "gather": {
     "logged": 1598546650307
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Run Id:  HD_1fadb825-c230-40c3-8751-c6332a21be96_4\n",
      "\n",
      " Accuracy: 0.9803921568627451\n",
      "\n",
      " Regularization Strength: 2.0\n",
      "\n",
      " Max Iterations: 150\n"
     ]
    }
   ],
   "source": [
    "best_run = hyperDrive_run.get_best_run_by_primary_metric()\n",
    "best_run_metrics = best_run.get_metrics()\n",
    "\n",
    "print('Best Run Id: ', best_run.id)\n",
    "print('\\n Accuracy:', best_run_metrics['Accuracy'])\n",
    "print('\\n Regularization Strength:',best_run_metrics['Regularization Strength:'])\n",
    "print('\\n Max Iterations:',best_run_metrics['Max iterations:'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "gather": {
     "logged": 1598546657829
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "# Code below registers the best model with the information of Metrics\n",
    "model = best_run.register_model(model_name='HyperDrive_HighAccuracy', model_path='outputs/', \n",
    "                                properties={'Accuracy': best_run_metrics['Accuracy'],\n",
    "                                            'Regularization Strength': best_run_metrics['Regularization Strength:'],\n",
    "                                           'Max Iterations': best_run_metrics['Max iterations:']})"
   ]
  }
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