Amazon SageMaker AutoPilot (model building to deploy).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amazon SageMaker AutoPilot (ML Tabular Problem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amazon SageMaker Autopilot automatically trains and tunes the best machine learning models for classification or regression, based on your data while allowing to maintain full control and visibility.\n",
    "\n",
    "Building machine learning (ML) models has traditionally required a binary choice. On one hand, you could manually prepare the features, select the algorithm, and optimize the model parameters in order to have full control over the model design and understand all the thought that went into creating it. However, this approach requires deep ML expertise. On the other hand, if you don’t have that expertise, you could use an automated approach (AutoML) to model generation that takes care of all of the heavy lifting, but provides very little visibility into how the model was created. While a model created with AutoML can work well, you may have less trust in it because you can’t understand what went into it, you can’t recreate it, and you can’t learn best practices which may help you in the future.\n",
    "\n",
    "Amazon SageMaker Autopilot eliminates this choice, allowing you to automatically build machine learning models without compromises. With SageMaker Autopilot, you provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![AUTOML]('./img/SageMaker-AutoML.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.64.0\n"
     ]
    }
   ],
   "source": [
    "import sagemaker\n",
    "import smdebug_rulesconfig as rule_configs\n",
    "import boto3\n",
    "import os\n",
    "import sys\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "print(sagemaker.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sagemaker session : <sagemaker.session.Session object at 0x00000163480C9E88>\n",
      "S3 bucket : hyunjoon-sagemaker-us\n",
      "Prefix : sagemaker/DEMO-automl-dm\n",
      "Region selected : us-west-2\n"
     ]
    }
   ],
   "source": [
    "sess   = sagemaker.Session()\n",
    "bucket = 'hyunjoon-sagemaker-us' # or sess.default_bucket()                     \n",
    "prefix = 'sagemaker/DEMO-automl-dm'\n",
    "region = boto3.Session().region_name\n",
    "\n",
    "print('Sagemaker session :', sess)\n",
    "print('S3 bucket :', bucket)\n",
    "print('Prefix :', prefix)\n",
    "print('Region selected :', region)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the direct marketing dataset from UCI's ML Repository.\n",
    "The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Download Dataset Manually here](https://archive.ics.uci.edu/ml/datasets/bank+marketing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wget Download\n",
    "!wget -N https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip \\\n",
    "!unzip -o bank-additional.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'C:\\\\Users\\\\bokhy\\\\Desktop\\\\Python\\\\github\\\\amazon-sagemaker\\\\data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  duration  campaign  pdays  previous     poutcome  \\\n",
       "0   may         mon       261         1    999         0  nonexistent   \n",
       "1   may         mon       149         1    999         0  nonexistent   \n",
       "2   may         mon       226         1    999         0  nonexistent   \n",
       "3   may         mon       151         1    999         0  nonexistent   \n",
       "4   may         mon       307         1    999         0  nonexistent   \n",
       "\n",
       "   emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0           1.1          93.994          -36.4      4.857       5191.0  no  \n",
       "1           1.1          93.994          -36.4      4.857       5191.0  no  \n",
       "2           1.1          93.994          -36.4      4.857       5191.0  no  \n",
       "3           1.1          93.994          -36.4      4.857       5191.0  no  \n",
       "4           1.1          93.994          -36.4      4.857       5191.0  no  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 500)     # Make sure we can see all of the columns\n",
    "pd.set_option('display.max_rows', 50)         # Keep the output on one page\n",
    "\n",
    "# Read data\n",
    "data = pd.read_csv(os.path.join(path, 'bank-additional-full.csv'), sep=';')\n",
    "data.head() # Show the first 10 lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. PreProcess the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset\n",
    "train_data, test_data, _ = np.split(data.sample(frac=1, random_state=123), \n",
    "                                                  [int(0.95 * len(data)), int(len(data))])  \n",
    "\n",
    "# Save to CSV files\n",
    "train_data.to_csv('./data/automl-train.csv', index=False, header=True, sep=',') # Need to keep column names\n",
    "test_data.to_csv('./data/automl-test.csv', index=False, header=True, sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Upload data to S3 bucket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No preprocessing on data is needed in SageMaker AutoML! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://sagemaker-us-west-2-570447867175/sagemaker/DEMO-automl-dm/input/automl-train.csv\n"
     ]
    }
   ],
   "source": [
    "s3_input_data = sess.upload_data(path=\"./data/automl-train.csv\", key_prefix=prefix + \"/input\")\n",
    "print(s3_input_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Setting up SageMaker AutoPilot training parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doc: https://sagemaker.readthedocs.io/en/stable/automl.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You would need to create a role in [IAM Console](https://console.aws.amazon.com/iam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For detailed instruction, please refer to this [AWS Developer Guie](https://docs.aws.amazon.com/glue/latest/dg/create-an-iam-role-sagemaker-notebook.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.automl.automl import AutoML\n",
    "\n",
    "\n",
    "role = 'arn:aws:iam::570447867175:role/SageMakerNotebookRole' # pass your IAM role name\n",
    "\n",
    "auto_ml_job = AutoML(\n",
    "    role = role,                                              # IAM role\n",
    "    sagemaker_session = sess,                                  \n",
    "    target_attribute_name = 'y',                              # The column we want to predict \n",
    "    output_path = 's3://{}/{}/output'.format(bucket,prefix),  # Save output artifacts here\n",
    "    max_candidates = 100,                                     # Number of models (Defaut: 500) \n",
    "    max_runtime_per_training_job_in_seconds = 600, \n",
    "    total_job_runtime_in_seconds = 3600\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Start Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'s3_input' class will be renamed to 'TrainingInput' in SageMaker Python SDK v2.\n"
     ]
    }
   ],
   "source": [
    "auto_ml_job.fit(inputs=s3_input_data, logs=False, wait=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AutoMLJobName': 'automl-2020-06-18-07-27-05-187',\n",
       " 'AutoMLJobArn': 'arn:aws:sagemaker:us-west-2:570447867175:automl-job/automl-2020-06-18-07-27-05-187',\n",
       " 'InputDataConfig': [{'DataSource': {'S3DataSource': {'S3DataType': 'S3Prefix',\n",
       "     'S3Uri': 's3://sagemaker-us-west-2-570447867175/sagemaker/DEMO-automl-dm/input/automl-train.csv'}},\n",
       "   'TargetAttributeName': 'y'}],\n",
       " 'OutputDataConfig': {'S3OutputPath': 's3://hyunjoon-sagemaker-us/sagemaker/DEMO-automl-dm/output'},\n",
       " 'RoleArn': 'arn:aws:iam::570447867175:role/SageMakerNotebookRole',\n",
       " 'AutoMLJobConfig': {'CompletionCriteria': {'MaxCandidates': 100,\n",
       "   'MaxRuntimePerTrainingJobInSeconds': 600,\n",
       "   'MaxAutoMLJobRuntimeInSeconds': 3600},\n",
       "  'SecurityConfig': {'EnableInterContainerTrafficEncryption': False}},\n",
       " 'CreationTime': datetime.datetime(2020, 6, 18, 0, 27, 5, 278000, tzinfo=tzlocal()),\n",
       " 'LastModifiedTime': datetime.datetime(2020, 6, 18, 0, 27, 7, 353000, tzinfo=tzlocal()),\n",
       " 'AutoMLJobStatus': 'InProgress',\n",
       " 'AutoMLJobSecondaryStatus': 'AnalyzingData',\n",
       " 'GenerateCandidateDefinitionsOnly': False,\n",
       " 'ResponseMetadata': {'RequestId': '83a30c7e-4605-4118-81e8-d016be8c5e54',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': '83a30c7e-4605-4118-81e8-d016be8c5e54',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '899',\n",
       "   'date': 'Thu, 18 Jun 2020 07:27:07 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_ml_job.describe_auto_ml_job()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracking the progress of the AutoPilot job \n",
    "SageMaker AutoPilot job consists of **four high-level steps** :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Data Preprocessing, where the dataset is split into train and validation sets. \\\n",
    "2. Recommending Pipelines, where the dataset is analyzed and SageMaker AutoPilot comes up with a list of ML pipelines that should be tried out on the dataset. \\\n",
    "3. Automatic Feature Engineering, where SageMaker AutoPilot performs feature transformation on individual features of the dataset as well as at an aggregate level. \\\n",
    "4. ML pipeline selection and hyperparameter tuning, where the top performing pipeline is selected along with the optimal hyperparameters for the training algorithm (the last stage of the pipeline). \\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress AnalyzingData\n",
      "InProgress FeatureEngineering\n",
      "Data analysis complete\n"
     ]
    }
   ],
   "source": [
    "from time import sleep\n",
    "\n",
    "job = auto_ml_job.describe_auto_ml_job()\n",
    "job_status = job['AutoMLJobStatus']\n",
    "job_sec_status = job['AutoMLJobSecondaryStatus']\n",
    "\n",
    "if job_status not in ('Stopped', 'Failed'):\n",
    "    while job_status in ('InProgress') and job_sec_status in ('AnalyzingData'):\n",
    "        sleep(30)\n",
    "        job = auto_ml_job.describe_auto_ml_job()\n",
    "        job_status = job['AutoMLJobStatus']\n",
    "        job_sec_status = job['AutoMLJobSecondaryStatus']\n",
    "        print (job_status, job_sec_status)\n",
    "    print(\"Data analysis complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing notebooks generated by SageMaker AutoPilot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once data analysis is complete, SageMaker AutoPilot generates **two notebooks**: \n",
    "1. Data exploration \\\n",
    "2. Candidate definition \\"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://hyunjoon-sagemaker-us/sagemaker/DEMO-automl-dm/output/automl-2020-06-18-07-27-05-187/sagemaker-automl-candidates/pr-1-c96ac63cfc0240aca57b6e1b4efe6d295f229d0ef34547408b311743d1/notebooks/SageMakerAutopilotCandidateDefinitionNotebook.ipynb\n",
      "s3://hyunjoon-sagemaker-us/sagemaker/DEMO-automl-dm/output/automl-2020-06-18-07-27-05-187/sagemaker-automl-candidates/pr-1-c96ac63cfc0240aca57b6e1b4efe6d295f229d0ef34547408b311743d1/notebooks/SageMakerAutopilotDataExplorationNotebook.ipynb\n"
     ]
    }
   ],
   "source": [
    "job = auto_ml_job.describe_auto_ml_job()\n",
    "job_candidate_notebook = job['AutoMLJobArtifacts']['CandidateDefinitionNotebookLocation']\n",
    "job_data_notebook = job['AutoMLJobArtifacts']['DataExplorationNotebookLocation']\n",
    "\n",
    "print(job_candidate_notebook)\n",
    "print(job_data_notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy these two notebooks and open these notebooks. There are useful information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run following commands in Jupyter notebook\n",
    "or in CMD (command prompt) in Windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: aws [options] <command> <subcommand> [<subcommand> ...] [parameters]\n",
      "To see help text, you can run:\n",
      "\n",
      "  aws help\n",
      "  aws <command> help\n",
      "  aws <command> <subcommand> help\n",
      "aws: error: the following arguments are required: paths\n",
      "usage: aws [options] <command> <subcommand> [<subcommand> ...] [parameters]\n",
      "To see help text, you can run:\n",
      "\n",
      "  aws help\n",
      "  aws <command> help\n",
      "  aws <command> <subcommand> help\n",
      "aws: error: the following arguments are required: paths\n"
     ]
    }
   ],
   "source": [
    "# !aws s3 cp s3://hyunjoon-sagemaker-us/sagemaker/DEMO-automl-dm/output/automl-2020-06-18-07-27-05-187/sagemaker-automl-candidates/pr-1-c96ac63cfc0240aca57b6e1b4efe6d295f229d0ef34547408b311743d1/notebooks/SageMakerAutopilotCandidateDefinitionNotebook.ipynb candidate.ipynb\n",
    "# !aws s3 cp s3://hyunjoon-sagemaker-us/sagemaker/DEMO-automl-dm/output/automl-2020-06-18-07-27-05-187/sagemaker-automl-candidates/pr-1-c96ac63cfc0240aca57b6e1b4efe6d295f229d0ef34547408b311743d1/notebooks/SageMakerAutopilotDataExplorationNotebook.ipynb DataExploration.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SageMaker AutoPilot then launches feature engineering, and prepares different training and validation datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature engineering complete\n"
     ]
    }
   ],
   "source": [
    "job = auto_ml_job.describe_auto_ml_job()\n",
    "job_status = job['AutoMLJobStatus']\n",
    "job_sec_status = job['AutoMLJobSecondaryStatus']\n",
    "\n",
    "if job_status not in ('Stopped', 'Failed'):\n",
    "    while job_status in ('InProgress') and job_sec_status in ('FeatureEngineering'):\n",
    "        sleep(30)\n",
    "        job = auto_ml_job.describe_auto_ml_job()\n",
    "        job_status = job['AutoMLJobStatus']\n",
    "        job_sec_status = job['AutoMLJobSecondaryStatus']\n",
    "        print (job_status, job_sec_status)\n",
    "    print(\"Feature engineering complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once feature engineering is finished, SageMaker AutoPilot launches Auto Model Tuning on the different candidates. While model tuning is running, we can explore its progress with SageMaker Experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of jobs:  25\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TrialComponentName</th>\n",
       "      <th>DisplayName</th>\n",
       "      <th>SourceArn</th>\n",
       "      <th>SageMaker.ImageUri</th>\n",
       "      <th>SageMaker.InstanceCount</th>\n",
       "      <th>SageMaker.InstanceType</th>\n",
       "      <th>SageMaker.VolumeSizeInGB</th>\n",
       "      <th>_tuning_objective_metric</th>\n",
       "      <th>alpha</th>\n",
       "      <th>colsample_bytree</th>\n",
       "      <th>eta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>lambda</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>min_child_weight</th>\n",
       "      <th>num_round</th>\n",
       "      <th>objective</th>\n",
       "      <th>subsample</th>\n",
       "      <th>SageMaker.ModelName</th>\n",
       "      <th>SageMaker.ModelPrimary.DataUrl</th>\n",
       "      <th>SageMaker.ModelPrimary.Image</th>\n",
       "      <th>processor_module</th>\n",
       "      <th>sagemaker_program</th>\n",
       "      <th>sagemaker_submit_directory</th>\n",
       "      <th>input_channel_mode</th>\n",
       "      <th>job_name</th>\n",
       "      <th>label_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-004-ea1f352a-a...</td>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-004-ea1f352a-a...</td>\n",
       "      <td>arn:aws:sagemaker:us-west-2:570447867175:train...</td>\n",
       "      <td>246618743249.dkr.ecr.us-west-2.amazonaws.com/s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ml.m5.4xlarge</td>\n",
       "      <td>50.0</td>\n",
       "      <td>validation:accuracy</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.593890</td>\n",
       "      <td>0.015010</td>\n",
       "      <td>50.795290</td>\n",
       "      <td>1.537836</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.774205</td>\n",
       "      <td>5.0</td>\n",
       "      <td>binary:logistic</td>\n",
       "      <td>0.607919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-003-11bb7b5c-a...</td>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-003-11bb7b5c-a...</td>\n",
       "      <td>arn:aws:sagemaker:us-west-2:570447867175:train...</td>\n",
       "      <td>246618743249.dkr.ecr.us-west-2.amazonaws.com/s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ml.m5.4xlarge</td>\n",
       "      <td>50.0</td>\n",
       "      <td>validation:accuracy</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.345105</td>\n",
       "      <td>0.002555</td>\n",
       "      <td>0.041776</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.019499</td>\n",
       "      <td>163.0</td>\n",
       "      <td>binary:logistic</td>\n",
       "      <td>0.598289</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-006-e57d8a5c-a...</td>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-006-e57d8a5c-a...</td>\n",
       "      <td>arn:aws:sagemaker:us-west-2:570447867175:train...</td>\n",
       "      <td>246618743249.dkr.ecr.us-west-2.amazonaws.com/s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ml.m5.4xlarge</td>\n",
       "      <td>50.0</td>\n",
       "      <td>validation:accuracy</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.593890</td>\n",
       "      <td>0.015010</td>\n",
       "      <td>50.795290</td>\n",
       "      <td>1.537836</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.774205</td>\n",
       "      <td>5.0</td>\n",
       "      <td>binary:logistic</td>\n",
       "      <td>0.607919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-002-2f2ca630-a...</td>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-002-2f2ca630-a...</td>\n",
       "      <td>arn:aws:sagemaker:us-west-2:570447867175:train...</td>\n",
       "      <td>246618743249.dkr.ecr.us-west-2.amazonaws.com/s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ml.m5.4xlarge</td>\n",
       "      <td>50.0</td>\n",
       "      <td>validation:accuracy</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>0.345105</td>\n",
       "      <td>0.002555</td>\n",
       "      <td>0.041776</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.019499</td>\n",
       "      <td>163.0</td>\n",
       "      <td>binary:logistic</td>\n",
       "      <td>0.598289</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-010-7363e360-a...</td>\n",
       "      <td>tuning-job-1-46cb7fcbd7294bf7be-010-7363e360-a...</td>\n",
       "      <td>arn:aws:sagemaker:us-west-2:570447867175:train...</td>\n",
       "      <td>246618743249.dkr.ecr.us-west-2.amazonaws.com/s...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>ml.m5.4xlarge</td>\n",
       "      <td>50.0</td>\n",
       "      <td>validation:accuracy</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.593890</td>\n",
       "      <td>0.015010</td>\n",
       "      <td>50.795290</td>\n",
       "      <td>1.537836</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.774205</td>\n",
       "      <td>5.0</td>\n",
       "      <td>binary:logistic</td>\n",
       "      <td>0.607919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  TrialComponentName  \\\n",
       "0  tuning-job-1-46cb7fcbd7294bf7be-004-ea1f352a-a...   \n",
       "1  tuning-job-1-46cb7fcbd7294bf7be-003-11bb7b5c-a...   \n",
       "2  tuning-job-1-46cb7fcbd7294bf7be-006-e57d8a5c-a...   \n",
       "3  tuning-job-1-46cb7fcbd7294bf7be-002-2f2ca630-a...   \n",
       "4  tuning-job-1-46cb7fcbd7294bf7be-010-7363e360-a...   \n",
       "\n",
       "                                         DisplayName  \\\n",
       "0  tuning-job-1-46cb7fcbd7294bf7be-004-ea1f352a-a...   \n",
       "1  tuning-job-1-46cb7fcbd7294bf7be-003-11bb7b5c-a...   \n",
       "2  tuning-job-1-46cb7fcbd7294bf7be-006-e57d8a5c-a...   \n",
       "3  tuning-job-1-46cb7fcbd7294bf7be-002-2f2ca630-a...   \n",
       "4  tuning-job-1-46cb7fcbd7294bf7be-010-7363e360-a...   \n",
       "\n",
       "                                           SourceArn  \\\n",
       "0  arn:aws:sagemaker:us-west-2:570447867175:train...   \n",
       "1  arn:aws:sagemaker:us-west-2:570447867175:train...   \n",
       "2  arn:aws:sagemaker:us-west-2:570447867175:train...   \n",
       "3  arn:aws:sagemaker:us-west-2:570447867175:train...   \n",
       "4  arn:aws:sagemaker:us-west-2:570447867175:train...   \n",
       "\n",
       "                                  SageMaker.ImageUri  SageMaker.InstanceCount  \\\n",
       "0  246618743249.dkr.ecr.us-west-2.amazonaws.com/s...                      1.0   \n",
       "1  246618743249.dkr.ecr.us-west-2.amazonaws.com/s...                      1.0   \n",
       "2  246618743249.dkr.ecr.us-west-2.amazonaws.com/s...                      1.0   \n",
       "3  246618743249.dkr.ecr.us-west-2.amazonaws.com/s...                      1.0   \n",
       "4  246618743249.dkr.ecr.us-west-2.amazonaws.com/s...                      1.0   \n",
       "\n",
       "  SageMaker.InstanceType  SageMaker.VolumeSizeInGB _tuning_objective_metric  \\\n",
       "0          ml.m5.4xlarge                      50.0      validation:accuracy   \n",
       "1          ml.m5.4xlarge                      50.0      validation:accuracy   \n",
       "2          ml.m5.4xlarge                      50.0      validation:accuracy   \n",
       "3          ml.m5.4xlarge                      50.0      validation:accuracy   \n",
       "4          ml.m5.4xlarge                      50.0      validation:accuracy   \n",
       "\n",
       "      alpha  colsample_bytree       eta      gamma    lambda  max_depth  \\\n",
       "0  0.001664          0.593890  0.015010  50.795290  1.537836       22.0   \n",
       "1  0.000296          0.345105  0.002555   0.041776  0.000250        7.0   \n",
       "2  0.001664          0.593890  0.015010  50.795290  1.537836       22.0   \n",
       "3  0.000296          0.345105  0.002555   0.041776  0.000250        7.0   \n",
       "4  0.001664          0.593890  0.015010  50.795290  1.537836       22.0   \n",
       "\n",
       "   min_child_weight  num_round        objective  subsample  \\\n",
       "0         31.774205        5.0  binary:logistic   0.607919   \n",
       "1          0.019499      163.0  binary:logistic   0.598289   \n",
       "2         31.774205        5.0  binary:logistic   0.607919   \n",
       "3          0.019499      163.0  binary:logistic   0.598289   \n",
       "4         31.774205        5.0  binary:logistic   0.607919   \n",
       "\n",
       "  SageMaker.ModelName SageMaker.ModelPrimary.DataUrl  \\\n",
       "0                 NaN                            NaN   \n",
       "1                 NaN                            NaN   \n",
       "2                 NaN                            NaN   \n",
       "3                 NaN                            NaN   \n",
       "4                 NaN                            NaN   \n",
       "\n",
       "  SageMaker.ModelPrimary.Image processor_module sagemaker_program  \\\n",
       "0                          NaN              NaN               NaN   \n",
       "1                          NaN              NaN               NaN   \n",
       "2                          NaN              NaN               NaN   \n",
       "3                          NaN              NaN               NaN   \n",
       "4                          NaN              NaN               NaN   \n",
       "\n",
       "  sagemaker_submit_directory input_channel_mode job_name label_col  \n",
       "0                        NaN                NaN      NaN       NaN  \n",
       "1                        NaN                NaN      NaN       NaN  \n",
       "2                        NaN                NaN      NaN       NaN  \n",
       "3                        NaN                NaN      NaN       NaN  \n",
       "4                        NaN                NaN      NaN       NaN  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sagemaker.analytics import ExperimentAnalytics, TrainingJobAnalytics\n",
    "\n",
    "exp = ExperimentAnalytics(\n",
    "    sagemaker_session=sess, \n",
    "    experiment_name=job['AutoMLJobName'] + '-aws-auto-ml-job',\n",
    ")\n",
    "\n",
    "df = exp.dataframe()\n",
    "print(\"Number of jobs: \", len(df))\n",
    "\n",
    "# Move metric to first column\n",
    "# df = pd.concat([df['ObjectiveMetric - Max'], df.drop(['ObjectiveMetric - Max'], axis=1)], axis=1)\n",
    "# Show top 5 jobs\n",
    "# df.sort_values('ObjectiveMetric - Max', ascending=0)[:5]\n",
    "df[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "InProgress ModelTuning\n",
      "Completed MaxCandidatesReached\n",
      "Model tuning complete\n"
     ]
    }
   ],
   "source": [
    "job = auto_ml_job.describe_auto_ml_job()\n",
    "job_status = job['AutoMLJobStatus']\n",
    "job_sec_status = job['AutoMLJobSecondaryStatus']\n",
    "\n",
    "if job_status not in ('Stopped', 'Failed'):\n",
    "    while job_status in ('InProgress') and job_sec_status in ('ModelTuning'):\n",
    "        sleep(30)\n",
    "        job = auto_ml_job.describe_auto_ml_job()\n",
    "        job_status = job['AutoMLJobStatus']\n",
    "        job_sec_status = job['AutoMLJobSecondaryStatus']\n",
    "        print (job_status, job_sec_status)\n",
    "    print(\"Model tuning complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Model Deploy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "automl-2020-06-18-07-27-05-187-18-08-28-52\n"
     ]
    }
   ],
   "source": [
    "from time import strftime, gmtime\n",
    "timestamp = strftime('%d-%H-%M-%S', gmtime())\n",
    "\n",
    "endpoint_name = job['AutoMLJobName']+'-'+timestamp\n",
    "\n",
    "print(endpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parameter image will be renamed to image_uri in SageMaker Python SDK v2.\n",
      "Parameter image will be renamed to image_uri in SageMaker Python SDK v2.\n",
      "Parameter image will be renamed to image_uri in SageMaker Python SDK v2.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------!"
     ]
    }
   ],
   "source": [
    "auto_ml_job.deploy(\n",
    "    initial_instance_count = 1,\n",
    "    instance_type = 'ml.m4.xlarge',\n",
    "    endpoint_name = endpoint_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Predict with the test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From https://gitlab.com/juliensimon/awsdevdays2020/-/blob/master/mls2/XGBoost-AutoPilot.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.predictor import csv_serializer, RealTimePredictor\n",
    "from sagemaker.content_types import CONTENT_TYPE_CSV\n",
    "\n",
    "predictor = RealTimePredictor(\n",
    "    endpoint=endpoint_name, \n",
    "    sagemaker_session=sess, \n",
    "    serializer=csv_serializer,\n",
    "    content_type=CONTENT_TYPE_CSV, \n",
    "    accept='text/csv'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 1769 53\n",
      "111 127\n",
      "0.9204 0.7056 0.9410 0.8064\n"
     ]
    }
   ],
   "source": [
    "tp = tn = fp = fn = count = 0\n",
    "\n",
    "with open('./data/automl-test.csv') as f:\n",
    "    lines = f.readlines()\n",
    "    for l in lines[1:]:   # Skip header\n",
    "        l = l.split(',')  # Split CSV line into feature array\n",
    "        label = l[-1]     # Store 'yes'/'no' label\n",
    "        l = l[:-1]        # Remove label\n",
    "        l = ','.join(l)   # Rebuild CSV line without label\n",
    "                \n",
    "        response = predictor.predict(l)\n",
    "        response = response.decode(\"utf-8\")\n",
    "        #print (\"label %s response %s\" %(label,response))\n",
    "\n",
    "        if 'yes' in label:\n",
    "            # Sample is positive\n",
    "            if 'yes' in response:\n",
    "                # True positive\n",
    "                tp=tp+1\n",
    "            else:\n",
    "                # False negative\n",
    "                fn=fn+1\n",
    "        else:\n",
    "            # Sample is negative\n",
    "            if 'no' in response:\n",
    "                # True negative\n",
    "                tn=tn+1\n",
    "            else:\n",
    "                # False positive\n",
    "                fp=fp+1\n",
    "        count = count+1\n",
    "        if (count % 100 == 0):   \n",
    "            sys.stdout.write(str(count)+' ')\n",
    "            \n",
    "#Confusion matrix\n",
    "print (\"%d %d\" % (tn, fp))\n",
    "print (\"%d %d\" % (fn, tp))\n",
    "\n",
    "accuracy  = (tp+tn)/(tp+tn+fp+fn)\n",
    "precision = tp/(tp+fp)\n",
    "recall    = tn/(tn+fn)\n",
    "f1        = (2*precision*recall)/(precision+recall)\n",
    "\n",
    "print (\"%.4f %.4f %.4f %.4f\" % (accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Close the SageMaker Instance "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To make sure we don't get charged after the training/inference is over, we have to delete the endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.delete_endpoint(predictor.endpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Also, SageMaker AutoML job creates many underlying artifacts such as dataset splits, preprocessing scripts, preprocessed data, etc. Let's delete them too!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker/DEMO-automl-dm/output/automl-2020-06-18-07-27-05-187\n"
     ]
    }
   ],
   "source": [
    "import boto3\n",
    "\n",
    "job_outputs_prefix = '{}/output/{}'.format(prefix, job['AutoMLJobName'])\n",
    "print(job_outputs_prefix)\n",
    "\n",
    "s3_bucket =boto3.resource('s3').Bucket(bucket)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   'HostId': 'tqogCt29afGNln2RemIpKQ1c3Y0H1+midZ12x+GhVGOlOvl+ZeW4S0tfVqKhUUAJRzVwDSOT0rw=',\n",
       "   'HTTPStatusCode': 200,\n",
       "   'HTTPHeaders': {'x-amz-id-2': 'tqogCt29afGNln2RemIpKQ1c3Y0H1+midZ12x+GhVGOlOvl+ZeW4S0tfVqKhUUAJRzVwDSOT0rw=',\n",
       "    'x-amz-request-id': '18C196F4C827B924',\n",
       "    'date': 'Thu, 18 Jun 2020 08:44:36 GMT',\n",
       "    'connection': 'close',\n",
       "    'content-type': 'application/xml',\n",
       "    'transfer-encoding': 'chunked',\n",
       "    'server': 'AmazonS3'},\n",
       "   'RetryAttempts': 0},\n",
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