autopilot/sagemaker_autopilot_abalone_parquet_input.ipynb

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   "source": [
    "# Regression with Amazon SageMaker Autopilot (Parquet input)\n",
    "\n",
    "This is the accompanying notebook for the blog post [Run AutoML experiments with large parquet datasets using Amazon SageMaker Autopilot](https://aws.amazon.com/blogs/machine-learning/run-automl-experiments-with-large-parquet-datasets-using-amazon-sagemaker-autopilot/). The example here is almost the same as [Regression with Amazon SageMaker XGBoost algorithm (Parquet)](../introduction_to_amazon_algorithms/xgboost_abalone/xgboost_parquet_input_training.ipynb).\n",
    "\n",
    "This notebook tackles the exact same problem with the same solution, but has been modified for a Parquet input to be trained in SageMaker Autopilot. The original notebook provides details of dataset and the machine learning use-case.\n",
    "\n",
    "This notebook was tested in Amazon SageMaker Studio on a ml.t3.medium instance with Python 3 (Data Science) kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install --upgrade boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import boto3\n",
    "import re\n",
    "import sagemaker\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "role = get_execution_role()\n",
    "region = boto3.Session().region_name\n",
    "\n",
    "# S3 bucket for saving code and model artifacts.\n",
    "# Feel free to specify a different bucket here if you wish.\n",
    "bucket = sagemaker.Session().default_bucket()\n",
    "prefix = \"sagemaker/DEMO-automl-parquet\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use [PyArrow](https://arrow.apache.org/docs/python/) library to store the Abalone dataset in the Parquet format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyarrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "s3 = boto3.client(\"s3\")\n",
    "# Download the dataset and load into a pandas dataframe\n",
    "FILE_NAME = \"abalone.csv\"\n",
    "s3.download_file(\"sagemaker-sample-files\", f\"datasets/tabular/uci_abalone/abalone.csv\", FILE_NAME)\n",
    "\n",
    "feature_names = [\n",
    "    \"Sex\",\n",
    "    \"Length\",\n",
    "    \"Diameter\",\n",
    "    \"Height\",\n",
    "    \"Whole weight\",\n",
    "    \"Shucked weight\",\n",
    "    \"Viscera weight\",\n",
    "    \"Shell weight\",\n",
    "    \"Rings\",\n",
    "]\n",
    "data = pd.read_csv(FILE_NAME, header=None, names=feature_names)\n",
    "\n",
    "data.to_parquet(\"abalone.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "sagemaker.Session().upload_data(\"abalone.parquet\", bucket=bucket, key_prefix=prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After setting the parameters, we kick off training, and poll for status until training is completed, which in this example, takes under 1 hour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "import time\n",
    "from time import gmtime, strftime\n",
    "\n",
    "job_name = \"autopilot-parquet-\" + strftime(\"%m-%d-%H-%M\", gmtime())\n",
    "print(\"AutoML job:\", job_name)\n",
    "\n",
    "create_auto_ml_job_params = {\n",
    "    \"AutoMLJobConfig\": {\n",
    "        \"CompletionCriteria\": {\n",
    "            \"MaxCandidates\": 50,\n",
    "        }\n",
    "    },\n",
    "    \"AutoMLJobName\": job_name,\n",
    "    \"InputDataConfig\": [\n",
    "        {\n",
    "            \"ContentType\": \"x-application/vnd.amazon+parquet\",\n",
    "            \"CompressionType\": \"None\",\n",
    "            \"DataSource\": {\n",
    "                \"S3DataSource\": {\n",
    "                    \"S3DataType\": \"S3Prefix\",\n",
    "                    \"S3Uri\": f\"s3://{bucket}/{prefix}/abalone.parquet\",\n",
    "                }\n",
    "            },\n",
    "            \"TargetAttributeName\": \"Rings\",\n",
    "        }\n",
    "    ],\n",
    "    \"OutputDataConfig\": {\"S3OutputPath\": f\"s3://{bucket}/{prefix}/output\"},\n",
    "    \"RoleArn\": role,\n",
    "}\n",
    "\n",
    "client = boto3.client(\"sagemaker\", region_name=region)\n",
    "client.create_auto_ml_job(**create_auto_ml_job_params)\n",
    "\n",
    "response = client.describe_auto_ml_job(AutoMLJobName=job_name)\n",
    "status = response[\"AutoMLJobStatus\"]\n",
    "secondary_status = response[\"AutoMLJobSecondaryStatus\"]\n",
    "print(f\"{status} - {secondary_status}\")\n",
    "\n",
    "while status != \"Completed\" and secondary_status != \"Failed\":\n",
    "    time.sleep(60)\n",
    "    response = client.describe_auto_ml_job(AutoMLJobName=job_name)\n",
    "    status = response[\"AutoMLJobStatus\"]\n",
    "    secondary_status = response[\"AutoMLJobSecondaryStatus\"]\n",
    "    print(f\"{status} - {secondary_status}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please refer to other Autopilot example notebooks such as [Direct Marketing with Amazon SageMaker Autopilot](sagemaker_autopilot_direct_marketing.ipynb) and [Customer Churn Prediction with Amazon SageMaker Autopilot](autopilot_customer_churn.ipynb) to see how to investigate details of each training, deploy the best candidate and run inference."
   ]
  }
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