{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Develop, Train, Optimize and Deploy Scikit-Learn Random Forest\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
    "\n",
    "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-west-2/sagemaker-python-sdk|scikit_learn_randomforest|Sklearn_on_SageMaker_end2end.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* Doc https://sagemaker.readthedocs.io/en/stable/using_sklearn.html\n",
    "* SDK https://sagemaker.readthedocs.io/en/stable/sagemaker.sklearn.html\n",
    "* boto3 https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#client\n",
    "\n",
    "In this notebook we show how to use Amazon SageMaker to develop, train, tune and deploy a Scikit-Learn based ML model (Random Forest). More info on Scikit-Learn can be found here https://scikit-learn.org/stable/index.html. We use the California Housing dataset, present in Scikit-Learn: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html. The California Housing dataset was originally published in:\n",
    "\n",
    "> Pace, R. Kelley, and Ronald Barry. \"Sparse spatial autoregressions.\" Statistics & Probability Letters 33.3 (1997): 291-297.\n",
    " \n",
    "**This sample is provided for demonstration purposes, make sure to conduct appropriate testing if derivating this code for your own use-cases!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import time\n",
    "import tarfile\n",
    "\n",
    "import boto3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sagemaker import get_execution_role\n",
    "import sagemaker\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "\n",
    "sm_boto3 = boto3.client(\"sagemaker\")\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "\n",
    "region = sess.boto_session.region_name\n",
    "\n",
    "bucket = sess.default_bucket()  # this could also be a hard-coded bucket name\n",
    "\n",
    "print(\"Using bucket \" + bucket)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data\n",
    "We load a dataset from sklearn, split it and send it to S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we use the California housing dataset\n",
    "data = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.data, data.target, test_size=0.25, random_state=42\n",
    ")\n",
    "\n",
    "trainX = pd.DataFrame(X_train, columns=data.feature_names)\n",
    "trainX[\"target\"] = y_train\n",
    "\n",
    "testX = pd.DataFrame(X_test, columns=data.feature_names)\n",
    "testX[\"target\"] = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX.to_csv(\"california_housing_train.csv\")\n",
    "testX.to_csv(\"california_housing_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# send data to S3. SageMaker will take training data from s3\n",
    "trainpath = sess.upload_data(\n",
    "    path=\"california_housing_train.csv\", bucket=bucket, key_prefix=\"sagemaker/sklearncontainer\"\n",
    ")\n",
    "\n",
    "testpath = sess.upload_data(\n",
    "    path=\"california_housing_test.csv\", bucket=bucket, key_prefix=\"sagemaker/sklearncontainer\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing a *Script Mode* script\n",
    "The below script contains both training and inference functionality and can run both in SageMaker Training hardware or locally (desktop, SageMaker notebook, on prem, etc). Detailed guidance here https://sagemaker.readthedocs.io/en/stable/using_sklearn.html#preparing-the-scikit-learn-training-script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile script.py\n",
    "\n",
    "import argparse\n",
    "import joblib\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "\n",
    "# inference functions ---------------\n",
    "def model_fn(model_dir):\n",
    "    clf = joblib.load(os.path.join(model_dir, \"model.joblib\"))\n",
    "    return clf\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    print(\"extracting arguments\")\n",
    "    parser = argparse.ArgumentParser()\n",
    "\n",
    "    # hyperparameters sent by the client are passed as command-line arguments to the script.\n",
    "    # to simplify the demo we don't use all sklearn RandomForest hyperparameters\n",
    "    parser.add_argument(\"--n-estimators\", type=int, default=10)\n",
    "    parser.add_argument(\"--min-samples-leaf\", type=int, default=3)\n",
    "\n",
    "    # Data, model, and output directories\n",
    "    parser.add_argument(\"--model-dir\", type=str, default=os.environ.get(\"SM_MODEL_DIR\"))\n",
    "    parser.add_argument(\"--train\", type=str, default=os.environ.get(\"SM_CHANNEL_TRAIN\"))\n",
    "    parser.add_argument(\"--test\", type=str, default=os.environ.get(\"SM_CHANNEL_TEST\"))\n",
    "    parser.add_argument(\"--train-file\", type=str, default=\"california_housing_train.csv\")\n",
    "    parser.add_argument(\"--test-file\", type=str, default=\"california_housing_test.csv\")\n",
    "    parser.add_argument(\n",
    "        \"--features\", type=str\n",
    "    )  # in this script we ask user to explicitly name features\n",
    "    parser.add_argument(\n",
    "        \"--target\", type=str\n",
    "    )  # in this script we ask user to explicitly name the target\n",
    "\n",
    "    args, _ = parser.parse_known_args()\n",
    "\n",
    "    print(\"reading data\")\n",
    "    train_df = pd.read_csv(os.path.join(args.train, args.train_file))\n",
    "    test_df = pd.read_csv(os.path.join(args.test, args.test_file))\n",
    "\n",
    "    print(\"building training and testing datasets\")\n",
    "    X_train = train_df[args.features.split()]\n",
    "    X_test = test_df[args.features.split()]\n",
    "    y_train = train_df[args.target]\n",
    "    y_test = test_df[args.target]\n",
    "\n",
    "    # train\n",
    "    print(\"training model\")\n",
    "    model = RandomForestRegressor(\n",
    "        n_estimators=args.n_estimators, min_samples_leaf=args.min_samples_leaf, n_jobs=-1\n",
    "    )\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # print abs error\n",
    "    print(\"validating model\")\n",
    "    abs_err = np.abs(model.predict(X_test) - y_test)\n",
    "\n",
    "    # print couple perf metrics\n",
    "    for q in [10, 50, 90]:\n",
    "        print(\"AE-at-\" + str(q) + \"th-percentile: \" + str(np.percentile(a=abs_err, q=q)))\n",
    "\n",
    "    # persist model\n",
    "    path = os.path.join(args.model_dir, \"model.joblib\")\n",
    "    joblib.dump(model, path)\n",
    "    print(\"model persisted at \" + path)\n",
    "    print(args.min_samples_leaf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local training\n",
    "Script arguments allows us to remove from the script any SageMaker-specific configuration, and run locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! python script.py --n-estimators 100 \\\n",
    "                   --min-samples-leaf 2 \\\n",
    "                   --model-dir ./ \\\n",
    "                   --train ./ \\\n",
    "                   --test ./ \\\n",
    "                   --features 'MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude Longitude' \\\n",
    "                   --target target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SageMaker Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Launching a training job with the Python SDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use the Estimator from the SageMaker Python SDK\n",
    "from sagemaker.sklearn.estimator import SKLearn\n",
    "\n",
    "FRAMEWORK_VERSION = \"0.23-1\"\n",
    "\n",
    "sklearn_estimator = SKLearn(\n",
    "    entry_point=\"script.py\",\n",
    "    role=get_execution_role(),\n",
    "    instance_count=1,\n",
    "    instance_type=\"ml.c5.xlarge\",\n",
    "    framework_version=FRAMEWORK_VERSION,\n",
    "    base_job_name=\"rf-scikit\",\n",
    "    metric_definitions=[{\"Name\": \"median-AE\", \"Regex\": \"AE-at-50th-percentile: ([0-9.]+).*$\"}],\n",
    "    hyperparameters={\n",
    "        \"n-estimators\": 100,\n",
    "        \"min-samples-leaf\": 3,\n",
    "        \"features\": \"MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude Longitude\",\n",
    "        \"target\": \"target\",\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# launch training job, with asynchronous call\n",
    "sklearn_estimator.fit({\"train\": trainpath, \"test\": testpath}, wait=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative: launching a training with `boto3`\n",
    "`boto3` is more verbose yet gives more visibility in the low-level details of Amazon SageMaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first compress the code and send to S3\n",
    "\n",
    "source = \"source.tar.gz\"\n",
    "project = \"scikitlearn-train-from-boto3\"\n",
    "\n",
    "tar = tarfile.open(source, \"w:gz\")\n",
    "tar.add(\"script.py\")\n",
    "tar.close()\n",
    "\n",
    "s3 = boto3.client(\"s3\")\n",
    "s3.upload_file(source, bucket, project + \"/\" + source)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using `boto3` to launch a training job we must explicitly point to a docker image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker import image_uris\n",
    "\n",
    "\n",
    "training_image = image_uris.retrieve(\n",
    "    framework=\"sklearn\",\n",
    "    region=region,\n",
    "    version=FRAMEWORK_VERSION,\n",
    "    py_version=\"py3\",\n",
    "    instance_type=\"ml.c5.xlarge\",\n",
    ")\n",
    "print(training_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# launch training job\n",
    "\n",
    "response = sm_boto3.create_training_job(\n",
    "    TrainingJobName=\"sklearn-boto3-\" + datetime.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"),\n",
    "    HyperParameters={\n",
    "        \"n_estimators\": \"300\",\n",
    "        \"min_samples_leaf\": \"3\",\n",
    "        \"sagemaker_program\": \"script.py\",\n",
    "        \"features\": \"MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude Longitude\",\n",
    "        \"target\": \"target\",\n",
    "        \"sagemaker_submit_directory\": \"s3://\" + bucket + \"/\" + project + \"/\" + source,\n",
    "    },\n",
    "    AlgorithmSpecification={\n",
    "        \"TrainingImage\": training_image,\n",
    "        \"TrainingInputMode\": \"File\",\n",
    "        \"MetricDefinitions\": [\n",
    "            {\"Name\": \"median-AE\", \"Regex\": \"AE-at-50th-percentile: ([0-9.]+).*$\"},\n",
    "        ],\n",
    "    },\n",
    "    RoleArn=get_execution_role(),\n",
    "    InputDataConfig=[\n",
    "        {\n",
    "            \"ChannelName\": \"train\",\n",
    "            \"DataSource\": {\n",
    "                \"S3DataSource\": {\n",
    "                    \"S3DataType\": \"S3Prefix\",\n",
    "                    \"S3Uri\": trainpath,\n",
    "                    \"S3DataDistributionType\": \"FullyReplicated\",\n",
    "                }\n",
    "            },\n",
    "        },\n",
    "        {\n",
    "            \"ChannelName\": \"test\",\n",
    "            \"DataSource\": {\n",
    "                \"S3DataSource\": {\n",
    "                    \"S3DataType\": \"S3Prefix\",\n",
    "                    \"S3Uri\": testpath,\n",
    "                    \"S3DataDistributionType\": \"FullyReplicated\",\n",
    "                }\n",
    "            },\n",
    "        },\n",
    "    ],\n",
    "    OutputDataConfig={\"S3OutputPath\": \"s3://\" + bucket + \"/sagemaker-sklearn-artifact/\"},\n",
    "    ResourceConfig={\"InstanceType\": \"ml.c5.xlarge\", \"InstanceCount\": 1, \"VolumeSizeInGB\": 10},\n",
    "    StoppingCondition={\"MaxRuntimeInSeconds\": 86400},\n",
    "    EnableNetworkIsolation=False,\n",
    ")\n",
    "\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Launching a tuning job with the Python SDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we use the Hyperparameter Tuner\n",
    "from sagemaker.tuner import IntegerParameter\n",
    "\n",
    "# Define exploration boundaries\n",
    "hyperparameter_ranges = {\n",
    "    \"n-estimators\": IntegerParameter(20, 100),\n",
    "    \"min-samples-leaf\": IntegerParameter(2, 6),\n",
    "}\n",
    "\n",
    "# create Optimizer\n",
    "Optimizer = sagemaker.tuner.HyperparameterTuner(\n",
    "    estimator=sklearn_estimator,\n",
    "    hyperparameter_ranges=hyperparameter_ranges,\n",
    "    base_tuning_job_name=\"RF-tuner\",\n",
    "    objective_type=\"Minimize\",\n",
    "    objective_metric_name=\"median-AE\",\n",
    "    metric_definitions=[\n",
    "        {\"Name\": \"median-AE\", \"Regex\": \"AE-at-50th-percentile: ([0-9.]+).*$\"}\n",
    "    ],  # extract tracked metric from logs with regexp\n",
    "    max_jobs=10,\n",
    "    max_parallel_jobs=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Optimizer.fit({\"train\": trainpath, \"test\": testpath})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get tuner results in a df\n",
    "results = Optimizer.analytics().dataframe()\n",
    "while results.empty:\n",
    "    time.sleep(1)\n",
    "    results = Optimizer.analytics().dataframe()\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy to a real-time endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy with Python SDK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An `Estimator` could be deployed directly after training, with an `Estimator.deploy()` but here we showcase the more extensive process of creating a model from s3 artifacts, that could be used to deploy a model that was trained in a different session or even out of SageMaker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sklearn_estimator.latest_training_job.wait(logs=\"None\")\n",
    "artifact = sm_boto3.describe_training_job(\n",
    "    TrainingJobName=sklearn_estimator.latest_training_job.name\n",
    ")[\"ModelArtifacts\"][\"S3ModelArtifacts\"]\n",
    "\n",
    "print(\"Model artifact persisted at \" + artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.sklearn.model import SKLearnModel\n",
    "\n",
    "model = SKLearnModel(\n",
    "    model_data=artifact,\n",
    "    role=get_execution_role(),\n",
    "    entry_point=\"script.py\",\n",
    "    framework_version=FRAMEWORK_VERSION,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = model.deploy(instance_type=\"ml.c5.large\", initial_instance_count=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Invoke with the Python SDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the SKLearnPredictor does the serialization from pandas for us\n",
    "print(predictor.predict(testX[data.feature_names]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative: invoke with `boto3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtime = boto3.client(\"sagemaker-runtime\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Option 1: `csv` serialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# csv serialization\n",
    "response = runtime.invoke_endpoint(\n",
    "    EndpointName=predictor.endpoint,\n",
    "    Body=testX[data.feature_names].to_csv(header=False, index=False).encode(\"utf-8\"),\n",
    "    ContentType=\"text/csv\",\n",
    ")\n",
    "\n",
    "print(response[\"Body\"].read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Option 2: `npy` serialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# npy serialization\n",
    "from io import BytesIO\n",
    "\n",
    "\n",
    "# Serialise numpy ndarray as bytes\n",
    "buffer = BytesIO()\n",
    "# Assuming testX is a data frame\n",
    "np.save(buffer, testX[data.feature_names].values)\n",
    "\n",
    "response = runtime.invoke_endpoint(\n",
    "    EndpointName=predictor.endpoint, Body=buffer.getvalue(), ContentType=\"application/x-npy\"\n",
    ")\n",
    "\n",
    "print(response[\"Body\"].read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Don't forget to delete the endpoint !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm_boto3.delete_endpoint(EndpointName=predictor.endpoint)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook CI Test Results\n",
    "\n",
    "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
    "\n",
    "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-east-1/sagemaker-python-sdk|scikit_learn_randomforest|Sklearn_on_SageMaker_end2end.ipynb)\n",
    "\n",
    "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-east-2/sagemaker-python-sdk|scikit_learn_randomforest|Sklearn_on_SageMaker_end2end.ipynb)\n",
    "\n",
    "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-west-1/sagemaker-python-sdk|scikit_learn_randomforest|Sklearn_on_SageMaker_end2end.ipynb)\n",
    "\n",
    "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ca-central-1/sagemaker-python-sdk|scikit_learn_randomforest|Sklearn_on_SageMaker_end2end.ipynb)\n",
    "\n",
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    "\n",
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