From ffef369c28375c62430e672836e39b7c4135f81c Mon Sep 17 00:00:00 2001 From: neelamkoshiya Date: Wed, 21 Sep 2022 10:38:08 -0700 Subject: [PATCH] Sagemaker DataWrangler Samples addition (#3510) * Create readme.md * Add files via upload Joined flow added * Add files via upload * Add files via upload * Add files via upload * Delete TS-Workshop-Advanced.ipynb * Delete TS-Workshop-Cleanup.ipynb * Delete TS-Workshop.ipynb * Add files via upload Updated after the CI errors * Create test.txt * Add files via upload * Delete sagemaker-datawrangler/timeseries-dataflow/pictures directory * Delete timeseries.flow * Add files via upload * Add files via upload * Add files via upload * Update index.rst * Add files via upload Added rst file for joined * Add files via upload added tabular index.rst file * Add files via upload Uploaded index.rst for time series data * Delete sagemaker-datawrangler/tabular-dataflow/img directory Images are now in S3 bucket so deleting this * Update README.md updating image links with s3 links * Update and rename sagemaker-datawrangler/tabular-dataflow/Data-Exploration.md to sagemaker-datawrangler/tabular-dataflow/data-exploration/Data-Exploration.md updating image link and folder * Add files via upload uploading index.rst * Update and rename sagemaker-datawrangler/tabular-dataflow/Data-Import.md to sagemaker-datawrangler/tabular-dataflow/data-import/Data-Import.md updated image links * Add files via upload index.rst for data import * Update Data-Transformations.md * Rename sagemaker-datawrangler/tabular-dataflow/Data-Transformations.md to sagemaker-datawrangler/tabular-dataflow/data-transformations/Data-Transformations.md * Add files via upload * Update readme.md * Delete sagemaker-datawrangler/joined-dataflow/img directory * Update readme.md * Delete sagemaker-datawrangler/timeseries-dataflow/img directory * Update index.rst * Update index.rst Updated index.rst to link to other files * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update README.md referring to /readme.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Add files via upload * Add files via upload * Update index.rst * Create index.rst * Update index.rst * Update index.rst * Add files via upload * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Delete sagemaker-datawrangler/import-flow directory * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst * Update index.rst added data wrangler to the prep section * Update index.rst * Update index.rst * Add files via upload Updated per comments from aqyt * Update explore_data.ipynb Updated per Amelia comment - present tense * Update index.rst Grammer * Update index.rst Grammer * Update index.rst * Update import-flow.md Co-authored-by: atqy <95724753+atqy@users.noreply.github.com> Co-authored-by: Aaron Markham --- index.rst | 9 +- sagemaker-datawrangler/README.md | 41 + sagemaker-datawrangler/import-flow.md | 11 + sagemaker-datawrangler/index.rst | 69 + .../joined-dataflow/explore_data.ipynb | 285 + .../joined-dataflow/index.rst | 701 + .../joined-dataflow/join.flow | 420 + .../joined-dataflow/readme.md | 323 + sagemaker-datawrangler/readme.md | 1 + .../Hotel-Bookings-Classification.flow | 242231 +++++++++++++++ .../tabular-dataflow/README.md | 126 + .../data-exploration/Data-Exploration.md | 242 + .../data-exploration/index.rst | 408 + .../data-import/Data-Import.md | 49 + .../tabular-dataflow/data-import/index.rst | 87 + .../Data-Transformations.md | 195 + .../data-transformations/index.rst | 301 + .../tabular-dataflow/index.rst | 361 + .../TS-Workshop-DataPreparation.flow | 585 + .../timeseries-dataflow/index.rst | 1050 + .../timeseries-dataflow/readme.md | 780 + sagemaker-datawrangler/uploadflow.png | Bin 0 -> 42693 bytes 22 files changed, 248274 insertions(+), 1 deletion(-) create mode 100644 sagemaker-datawrangler/README.md create mode 100644 sagemaker-datawrangler/import-flow.md create mode 100644 sagemaker-datawrangler/index.rst create mode 100644 sagemaker-datawrangler/joined-dataflow/explore_data.ipynb create mode 100644 sagemaker-datawrangler/joined-dataflow/index.rst create mode 100644 sagemaker-datawrangler/joined-dataflow/join.flow create mode 100644 sagemaker-datawrangler/joined-dataflow/readme.md create mode 100644 sagemaker-datawrangler/readme.md create mode 100644 sagemaker-datawrangler/tabular-dataflow/Hotel-Bookings-Classification.flow create mode 100644 sagemaker-datawrangler/tabular-dataflow/README.md create mode 100644 sagemaker-datawrangler/tabular-dataflow/data-exploration/Data-Exploration.md create mode 100644 sagemaker-datawrangler/tabular-dataflow/data-exploration/index.rst create mode 100644 sagemaker-datawrangler/tabular-dataflow/data-import/Data-Import.md create mode 100644 sagemaker-datawrangler/tabular-dataflow/data-import/index.rst create mode 100644 sagemaker-datawrangler/tabular-dataflow/data-transformations/Data-Transformations.md create mode 100644 sagemaker-datawrangler/tabular-dataflow/data-transformations/index.rst create mode 100644 sagemaker-datawrangler/tabular-dataflow/index.rst create mode 100644 sagemaker-datawrangler/timeseries-dataflow/TS-Workshop-DataPreparation.flow create mode 100644 sagemaker-datawrangler/timeseries-dataflow/index.rst create mode 100644 sagemaker-datawrangler/timeseries-dataflow/readme.md create mode 100644 sagemaker-datawrangler/uploadflow.png diff --git a/index.rst b/index.rst index 63cf0ba10f..e153475283 100644 --- a/index.rst +++ b/index.rst @@ -39,6 +39,7 @@ We recommend the following notebooks as a broad introduction to the capabilities :maxdepth: 1 :caption: Prepare data + sagemaker-datawrangler/index sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing_outputs sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing_outputs @@ -210,10 +211,16 @@ More examples sagemaker-clarify/index scientific_details_of_algorithms/index aws_marketplace/index + .. toctree:: :maxdepth: 1 :caption: Community examples - contrib/index \ No newline at end of file + contrib/index + + + + + diff --git a/sagemaker-datawrangler/README.md b/sagemaker-datawrangler/README.md new file mode 100644 index 0000000000..d6c963f90d --- /dev/null +++ b/sagemaker-datawrangler/README.md @@ -0,0 +1,41 @@ +![Amazon SageMaker Data Wrangler](https://github.com/aws/amazon-sagemaker-examples/raw/main/_static/sagemaker-banner.png) + +# Amazon SageMaker Data Wrangler Examples + +Example flows that demonstrate how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler. + +## :books: Background + +[Amazon SageMaker Data Wrangler](https://aws.amazon.com/sagemaker/data-wrangler/) reduces the time it takes to aggregate and prepare data for ML. From a single interface in SageMaker Studio, you can import data from Amazon S3, Amazon Athena, Amazon Redshift, AWS Lake Formation, and Amazon SageMaker Feature Store, and in just a few clicks SageMaker Data Wrangler will automatically load, aggregate, and display the raw data. It will then make conversion recommendations based on the source data, transform the data into new features, validate the features, and provide visualizations with recommendations on how to remove common sources of error such as incorrect labels. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines or import that data into Amazon SageMaker Feature Store. + + + +The [SageMaker example notebooks](https://sagemaker-examples.readthedocs.io/en/latest/) are Jupyter notebooks that demonstrate the usage of Amazon SageMaker. + +## :hammer_and_wrench: Setup + +Amazon SageMaker Data Wrangler is a feature in Amazon SageMaker Studio. Use this section to learn how to access and get started using Data Wrangler. Do the following: + +* Complete each step in [Prerequisites](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-prerequisite). + +* Follow the procedure in [Access Data Wrangler](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-access) to start using Data Wrangler. + + + + +## :notebook: Examples + +### **[Tabular DataFlow](tabular-dataflow/README.md)** + +This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Tabular dataset. + +### **[Timeseries DataFlow](timeseries-dataflow/readme.md)** + +This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Timeseries dataset. + +### **[Joined DataFlow](joined-dataflow/readme.md)** + +This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Joined dataset. + + + diff --git a/sagemaker-datawrangler/import-flow.md b/sagemaker-datawrangler/import-flow.md new file mode 100644 index 0000000000..772ea7f5fd --- /dev/null +++ b/sagemaker-datawrangler/import-flow.md @@ -0,0 +1,11 @@ +## Import Flow + +Each of the example has a flow file available which you can directly import to expedite the process or validate the flow. + +Here are the steps to import the flow + +* Download the flow file + +* In Sagemaker Studio, drag and drop the flow file or use the upload button to browse the flow and upload + +![uploadflow](/uploadflow.png) diff --git a/sagemaker-datawrangler/index.rst b/sagemaker-datawrangler/index.rst new file mode 100644 index 0000000000..da69caa39c --- /dev/null +++ b/sagemaker-datawrangler/index.rst @@ -0,0 +1,69 @@ + + +Amazon SageMaker Data Wrangler +======================================= + +These example flows demonstrates how to aggregate and prepare data for +Machine Learning using Amazon SageMaker Data Wrangler. + + +------------------ + +`Amazon SageMaker Data +Wrangler `__ reduces +the time it takes to aggregate and prepare data for ML. From a single +interface in SageMaker Studio, you can import data from Amazon S3, +Amazon Athena, Amazon Redshift, AWS Lake Formation, and Amazon SageMaker +Feature Store, and in just a few clicks SageMaker Data Wrangler will +automatically load, aggregate, and display the raw data. It will then +make conversion recommendations based on the source data, transform the +data into new features, validate the features, and provide +visualizations with recommendations on how to remove common sources of +error such as incorrect labels. Once your data is prepared, you can +build fully automated ML workflows with Amazon SageMaker Pipelines or +import that data into Amazon SageMaker Feature Store. + +The `SageMaker example +notebooks `__ are +Jupyter notebooks that demonstrate the usage of Amazon SageMaker. + +Setup +------------------------- + +Amazon SageMaker Data Wrangler is a feature in Amazon SageMaker Studio. +Use this section to learn how to access and get started using Data +Wrangler. Do the following: + +- Complete each step in + `Prerequisites `__. + +- Follow the procedure in `Access Data + Wrangler `__ + to start using Data Wrangler. + +Examples +------------------- + +Tabular Dataflow +--------------------------- + +.. toctree:: + :maxdepth: 1 + + tabular-dataflow/index + +Timeseries Dataflow +---------------------------- + +.. toctree:: + :maxdepth: 1 + + timeseries-dataflow/index + +Joined Dataflow +---------------------------- + +.. toctree:: + :maxdepth: 1 + + joined-dataflow/index diff --git a/sagemaker-datawrangler/joined-dataflow/explore_data.ipynb b/sagemaker-datawrangler/joined-dataflow/explore_data.ipynb new file mode 100644 index 0000000000..76d0173663 --- /dev/null +++ b/sagemaker-datawrangler/joined-dataflow/explore_data.ipynb @@ -0,0 +1,285 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Explore and Prepare Data for SageMaker DataWrangler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "## Background\n", + "In this notebook, we download and explore the data that is used to build the SageMaker DataWrangler flow file for data processing. After running this notebook, you can follow the [README.md](README.md) for the step by step instructions how to write the SageMaker DataWrangler .flow file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# update pandas to avoid data type issues in older 1.0 version\n", + "!pip install pandas --upgrade --quiet\n", + "import pandas as pd\n", + "\n", + "print(pd.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create data folder\n", + "!mkdir data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Prerequisites: Get Data \n", + "\n", + "----\n", + "\n", + "Here, we download the music data from a public S3 bucket. We then upload it to your default S3 bucket, which was created for you when you initially created a SageMaker Studio workspace. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we import the necessary python libraries and set up the environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import json\n", + "import sagemaker\n", + "import boto3\n", + "import os\n", + "from awscli.customizations.s3.utils import split_s3_bucket_key\n", + "\n", + "# Sagemaker session\n", + "sess = sagemaker.Session()\n", + "# get session bucket name\n", + "bucket = sess.default_bucket()\n", + "# bucket prefix or the subfolder for everything we produce\n", + "prefix = \"music-recommendation-demo\"\n", + "# s3 client\n", + "s3_client = boto3.client(\"s3\")\n", + "\n", + "print(f\"this is your default SageMaker Studio bucket name: {bucket}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# define the functions that will be used to download data\n", + "def get_data(public_s3_data, to_bucket, sample_data=1):\n", + " new_paths = []\n", + " for f in public_s3_data:\n", + " bucket_name, key_name = split_s3_bucket_key(f)\n", + " filename = f.split(\"/\")[-1]\n", + " new_path = \"s3://{}/{}/input/{}\".format(to_bucket, prefix, filename)\n", + " new_paths.append(new_path)\n", + "\n", + " # only download if not already downloaded\n", + " if not os.path.exists(\"./data/{}\".format(filename)):\n", + " # download s3 data\n", + " print(\"Downloading file from {}\".format(f))\n", + " s3_client.download_file(bucket_name, key_name, \"./data/{}\".format(filename))\n", + "\n", + " # subsample the data to create a smaller datatset for this demo\n", + " new_df = pd.read_csv(\"./data/{}\".format(filename))\n", + " new_df = new_df.sample(frac=sample_data)\n", + " new_df.to_csv(\"./data/{}\".format(filename), index=False)\n", + "\n", + " # upload s3 data to our default s3 bucket for SageMaker Studio\n", + " print(\"Uploading {} to {}\\n\".format(filename, new_path))\n", + " s3_client.upload_file(\n", + " \"./data/{}\".format(filename), to_bucket, os.path.join(prefix, \"input\", filename)\n", + " )\n", + "\n", + " return new_paths" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# public S3 bucket that contains our music data\n", + "s3_bucket_music_data = \"s3://sagemaker-sample-files/datasets/tabular/synthetic-music\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_data_paths = get_data(\n", + " [f\"{s3_bucket_music_data}/tracks.csv\", f\"{s3_bucket_music_data}/ratings.csv\"],\n", + " bucket,\n", + " sample_data=0.70,\n", + ")\n", + "print(new_data_paths)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# these are the new file paths located on your SageMaker Studio default s3 storage bucket\n", + "tracks_data_source = f\"s3://{bucket}/{prefix}/input/tracks.csv\"\n", + "ratings_data_source = f\"s3://{bucket}/{prefix}/input/ratings.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Explore the Data\n", + "\n", + "\n", + "##### [back to top](#00-nb)\n", + "\n", + "\n", + "----\n", + "\n", + "In this section, we perform preliminary data exploration to understand the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracks = pd.read_csv(\"./data/tracks.csv\")\n", + "ratings = pd.read_csv(\"./data/ratings.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the [pandas DataFrame head function](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.head.html) to view the first five rows in each of the dataframes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tracks.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ratings.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print the numbers of unique songs/tracks, users and user rating events\n", + "print(\"{:,} different songs/tracks\".format(tracks[\"trackId\"].nunique()))\n", + "print(\"{:,} users\".format(ratings[\"userId\"].nunique()))\n", + "print(\"{:,} user rating events\".format(ratings[\"ratingEventId\"].nunique()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot a bar chart to display the number of tracks per genre to see the distribution\n", + "tracks.groupby(\"genre\")[\"genre\"].count().plot.bar(title=\"Tracks by Genre\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the histogram to view the distribution of the number of ratings by user id\n", + "ratings[[\"ratingEventId\", \"userId\"]].plot.hist(\n", + " by=\"userId\", bins=50, title=\"Distribution of # of Ratings by User\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "\n", + "After you completed running this notebook, you can follow the steps in the README to start building the DataWrangler flow file." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (Data Science)", + "language": "python", + "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/datascience-1.0" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/sagemaker-datawrangler/joined-dataflow/index.rst b/sagemaker-datawrangler/joined-dataflow/index.rst new file mode 100644 index 0000000000..f3af102687 --- /dev/null +++ b/sagemaker-datawrangler/joined-dataflow/index.rst @@ -0,0 +1,701 @@ + +Joined Dataflow Example +============================ +The example provide quick walkthrough of how to aggregate and prepare +data for Machine Learning using Amazon SageMaker Data Wrangler for +joined dataset. + + +Music Recommendation Example +------------------------------ + +With the rapid growth of the commercial music streaming services, more +and more people nowadayas are listening to music from they model +devices. However, organising, managing and searching among all the +digital music produced by the society is a very time-cosuming and +tedious task. Using a music recommender system to predict the user’s +choices and suggest songs that is likely to be interesting has been a +common practice used by the music providers. + +Background +---------- + +Amazon SageMaker helps data scientists and developers to prepare, build, +train, and deploy machine learning models quickly by bringing together a +broad set of purpose-built capabilities. This example shows how +SageMaker can accelerate machine learning development during the data +preprocessing stage to help build the musical playlist tailored to a +user’s tastes. + +Dataset +~~~~~~~ + +.. container:: alert alert-block alert-info + + Dataset + + The dataset is required before we begin, ensure that you have + downloaded it by following the instructions below. + +Example track (track.csv) and user ratings (ratings.csv) data is +provided on a publicly available S3 bucket found here: +**s3://sagemaker-sample-files/datasets/tabular/synthetic-music** We’ll +be running a notebook to download the data in the demo so no need to +manually download it from here just yet. + +**tracks.csv** + ++---------------------------------------+------------------------------+ +| **Column name** | **Description** | ++=======================================+==============================+ +| ``trackId`` | unique identifier for each | +| | song/track | ++---------------------------------------+------------------------------+ +| ``length`` | song length in seconds | +| | (numerical) | ++---------------------------------------+------------------------------+ +| ``energy`` | Energy is a measure from 0.0 | +| | to 1.0 and represents a | +| | perceptual measure of | +| | intensity and activity | +| | (numerical) | ++---------------------------------------+------------------------------+ +| ``acousticness`` | A confidence measure from | +| | 0.0 to 1.0 of whether the | +| | track is acoustic. | +| | (numerical) | ++---------------------------------------+------------------------------+ +| ``valence`` | A measure from 0.0 to 1.0 | +| | describing the musical | +| | positiveness conveyed by a | +| | track. (numerical) | ++---------------------------------------+------------------------------+ +| ``speechiness`` | Speechiness detects the | +| | presence of spoken words in | +| | a track. (numerical) | ++---------------------------------------+------------------------------+ +| ``instrumentalness`` | Predicts whether a track | +| | contains no vocals. | +| | (numerical) | ++---------------------------------------+------------------------------+ +| ``liveness`` | Detects the presence of an | +| | audience in the recording. | +| | (numerical) | ++---------------------------------------+------------------------------+ +| ``tempo`` | The overall estimated tempo | +| | of a track in beats per | +| | minute (BPM). (numerical) | ++---------------------------------------+------------------------------+ +| ``genre`` | The genre of the song. For | +| | example, pop, rock etc. | +| | (categorical) | ++---------------------------------------+------------------------------+ + +**ratings.csv** + ++-------------------+-------------------------------------------------+ +| **Column name** | **Description** | ++===================+=================================================+ +| ``ratingEventId`` | unique identifier for each rating | ++-------------------+-------------------------------------------------+ +| ``ts`` | timestamp of rating event (datetime in seconds | +| | since 1970) | ++-------------------+-------------------------------------------------+ +| ``userId`` | unique id for each user | ++-------------------+-------------------------------------------------+ +| ``trackId`` | unique id for each song/track | ++-------------------+-------------------------------------------------+ +| ``sessionId`` | unique id for the user’s session | ++-------------------+-------------------------------------------------+ +| ``Rating`` | user’s rating of song on scale from 1 to 5 | ++-------------------+-------------------------------------------------+ + +For this example, we’ll be using our own generated track and user +ratings data, but publicly available datasets/apis such as the `Million +Song Dataset `__ and open-source song +ratings APIs are available for personal research purposes. A full +end-to-end pipeline can be found in this `SageMaker +example `__. + +Pre-requisites: +--------------- + +We need to ensure dataset (tracks and ratings dataset) for ML is +uploaded to a data source (instructions to download the dataset to +Amazon S3 is available in the following section). +Data source can be any one of the following options: + +* S3 +* Athena +* RedShift +* SnowFlake + +.. container:: alert alert-block alert-info + +Data Source + +For this experiment the Data Source will be `Amazon +S3 `__ + +Experiment steps +---------------- + +Downloading the dataset, and notebooks +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Ensure that you have a working `Amazon SageMaker +Studio `__ environment and +that it has been updated. + +Follow the steps below to download the dataset. + +Start with the ``explore-dataset.ipynb`` SageMaker Studio +notebook. + +* Explore the dataset locally by running the cells in the +notebook. +* Upload the datasets (CSV files) to an S3 location for +consumption by SageMaker Data Wrangler later. +* Copy the S3 URLs of the tracks and ratings data files to your +clipboard. We will use these URLs later to import these part +files into Data Wrangler and join them. + +Let’s run though each of the steps above. + +Double click on the file called explore-data.ipynb. Amazon SageMaker +may prompt you to select a kernel and image. If it does select Data +Science as the image and Python 3 as the Kernel, as shown here: +|image| + +You have now successfully downloaded the data and opened a notebook, +we will now upload the data to your S3 bucket. Note: An Amazon S3 +bucket was created for you when the Amazon SageMaker Studio +environment was started. +|image1| + +From the menu select **Run / Run All Cells** to execute all the cells +in the notebook. +|image2| + +Examine the results, as you can see the samples of the data are +printed to the screen. +|image3| + +The data structure is defined in the next section. However, before we +continue, note the path to the various datasets. Your paths will be +different to the ones in the image below. Please copy these paths as +you will use them later. An example of a path is +s3://sagemaker-eu-west-1-112233445566/music-recommendation-demo/input/tracks.csv +|image4| + +.. container:: alert alert-block alert-info + +Note + +Please also check the S3 location to make sure the files are uploaded +successfully before moving to the next section. + +In the next section we will import the datasets into Data Wrangler via +the SageMaker Studio User Interface (UI). + +Importing datasets from a data source (S3) to Data Wrangler +----------------------------------------------------------- + +Initialize SageMaker Data Wrangler via SageMaker Studio UI. + +There are two ways that you can do this, either from the Launcher +screen as depicted here: +|image5| + +Or from the SageMaker resources menu on the left, selecting Data +Wrangler, and new flow +|image6| +|image7| + +It takes a few minutes to load. +|image8| + +Once Data Wrangler is loaded, you should be able to see it under +running instances and apps as shown below. +|image9| + +Next, make sure you have copied the data paths when running the +``explore_data.ipynb`` notebook from the previous section (see +section: **Downloading the dataset, and notebooks**), as you will +need them in this section. +Once Data Wrangler is up and running, you can see the following data +flow interface with options for import, creating data flows and +export as shown below. +|image10| + +Make sure to rename the untitled.flow to your preference (for e.g., +join.flow) +Paste the S3 URL for the tracks.csv file into the search box below +and hit go. +|image11| + +Select the CSV file from the drop down results. On the right pane, +make sure COMMA is chosen as the delimiter and Sampling is *None*. +Hit *import* to import this dataset to Data Wrangler. +|image12| + +Once the dataset is imported, the Data flow interface looks as shown +below. +|image13| + +Since currently you are in the data flow tab, hit the import tab +(left of data flow tab) as seen in the above image. +Import the second part file (ratings.csv) following the same set of +instructions as noted previously. +|image14| + +Transform tracks dataset +~~~~~~~~~~~~~~~~~~~~~~~~ + +We firstly want to perform some data transformation using Data +Wrangler. Let us walkthough how to perform different transformations +using built-in and custom formula functionality in Data Wrangler. + +As the *genre* column in the tracks dataset is a categorical +feature, we need to perform one-hot encoding to trasform this +feature. +Click on the tracks file transform block as show in the image +below and select **Add transform**: +|image15| + +This takes us to the Data Wrangler transformations interface where +there are over 300+ transformations you can apply to your dataset. +Select **Add step** as shown below. +|image16| + +In the **ADD TRANFORM** window, double click the option **Encode +categorical**. +|image17| + +Then on the **ENCODE CATEGORICAL** window, choose *One-hot encode* +as the Transform type, *genre* as the input columns, and *Columns* +as the output style. Click *Preview* and the output is shown as +below: +|image18| + +Click **Add** to add the tranform step to the flow. If you go back +to the *Data Flow*, you can see the step has been added. +|image19| + +We also want to generate a new feature based on the danceability of +the track. Danceability describes how suitable a track is for dancing +based on a combination of musical elements including tempo, rhythm +stability, beat strength, and overall regularity. + +* Click on the newly added **One-hot encode** step and select *Add +transformation*: +|image20| + +Select **Add step** and choose **Custom formula**. +|image21| + +Copy and paste below formula and put **danceability** to the +*Output Column*. + +0.3*valence + 0.1*liveness + 0.1*energy + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-16.png + + + +Click **Preview** and **Add** the step to the flow. +|image22| + +Joining datasets - first join +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Given, we have imported both the tracks and ratings CSV files in the +beginning steps. Let us walk through on how to join these CSV files +based on a common unique identifier column, *trackId*. Then we will +perform some feature engineering to generate a new set of features +that can help to enrich the trainig data. + +Click on either the tracks or ratings transform block as shown in the +image blow: + +Here, we have selected tracks transform flow block and hit +**Join** +|image23| + +Select the other rating file transform block and it automatically +maps (converges) both the files into a Join preview as shown +below. + +.. raw:: html + +
+ + Note + + Files can also be concatenated similar to join operations + + .. raw:: html + +
+ +Hit configure. +|image24| + +Here, choose a name for the resulting join file and choose the +type of join and columns on to join (Please refer to the image +below). +|image25| + +Hit **Apply** (*Preview*) . You can see a preview of the Joined +dataset as shown in the image below. +|image26| + +.. raw:: html + +
+ +Note + +Depending on the version of SageMaker it might be Preview and not +Add + +.. raw:: html + +
+ +Hit *Add* at the upper right corner to add this Join transform to +the original data flow. + +At the end of this step, the data flow looks as shown below. +|image27| + +Next step, let’s see how to use Data Wrangler to add custom +transform to perform more advanced feature engineering. Here, we +want to use pyspark to calculate the average values of 5-star +ratings for different columns and use them as new features. + +Select on the jointrackid.csv block and click the + icon, under +which click on Add transform. +|image28| + +Click the custom transform at this step. +|image29| + +Give a name to this custom transform step and copy and paste below +custom script to the window. + +.. code:: python + + # Table is available as variable `df` + import pyspark.sql.functions as F + # numeric features + num_feat_cols = ['energy', 'acousticness', 'valence', 'speechiness', 'instrumentalness', 'liveness', 'tempo', 'danceability', 'genre_Latin', 'genre_Folk', 'genre_Blues', 'genre_Rap', 'genre_Reggae', 'genre_Jazz', 'genre_RnB', 'genre_Country', 'genre_Electronic', 'genre_Pop_Rock'] + # calculate averages for 5-star ratings + agg_obj = [F.avg(feat_col).alias(feat_col+"_5star") for feat_col in num_feat_cols] + df = df.filter(F.col('Rating')==5).groupBy('userId').agg(*agg_obj) + +- .. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-25.png + + + + Click *Preview* and the *Add* this step to the flow. + +We want to join the generated new features back to the original data. +Before joining back, we notice some columns in the joint dataset are +not needed for the model training, such as the id related columns. +Let’s see now how to add a simple transform using Data Wrangler to +drop the columns after the JOIN operation we did previously. + +Select the jointrackid.csv block and select *Add transform*. +|image30| + +Let us apply the manage columns transform to drop some columns +listed as below + +*trackId_0* +*trackId_1* +*ts* +*sessionId* +*itemInSession* +*ratingEventId* + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-27.png + + +image + +we can drop multiple columns by selecting each column from the +drop down manual. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-28.png + + + +Once all the columns are selected, hit **Preview** first and then +**Add**. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-29.png + + + + +Now go back to data flow. + +You should now be able to see the 2 transforms (custom transform +and dropping the columns) as shown below in the Data Flow +interface. +|image31| + +Next step is to join the two dataset back together. Similarly as +the first join, we select one block and choose *Join*. +|image32| + +Select the other file transform block and get a Join preview. +|image33| + +Fill in the step Name, Join Type and columns to join on +(*UserId*). +|image34| + +Preview and Add this step to the flow file. When we go back to the +data flow, this is how the flow looks like now. +|image35| + +After joining the two data source, we also want to drop the +*userId* columns and move the target column *Rating* to the first +column. + +Similar to the previous manage columns transform instructions, we +add two transform steps to drop the *userId_0* and *userId_1* +columns, and then move the *Rating* step to the start of the +table. + +|image36| + +|image37| + +Once all the transform steps are finished, we will export the +transformed data. SageMaker Data Wrangler also allow you to split +your dataset into train and test based on the ratio you set. + +To split the dataset, add another transform step and choose +**Split data**. +|image38| + +We choose *Randomized split* and get 80% for training and 20% data +for testing. +|image39| + +The data flow now looks as below: +|image40| + + +Export transformed features to S3 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To export the transformed dataset, first click on the + symbol and +choose Add Destination, followed by Amazon S3 as pointed out by the +screen shot below. + +|image41| + +A new window is opened, Click Export data, choose the S3 location +where you want to save the transformed dataset. + +|image42| + +Follow the same step to set the S3 location for the test data. + +A job is needed to export the data to Amazon S3, to do this press the +Create Job button on the top right, this will open a window. + +Set the Job name to something like generate-train-test-data + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-42.png + + + +Hit the **Configure Job** button at the bottom + +Leave the default instance type, and press the Run button at the +bottom. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-43.png + + + + +Note that your job has been created successfully and if you want to +see the progress of the job you can do so by following the link to +the generate-train-test-data process. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-44.png + + + + +Follow the link to see the status of your job. This processing job +takes around 5-10 mins. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-45.png + + + + +When the job is complete the train and test output files will be +available in the corresponding S3 output folders. You can find the +output location from the processing job configurations. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-processing-job-output.png + + + + +Other ways to export the transformations and analysis +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The join.flow file that we created initially captures all of the +transformations, joins and analysis. + +In a way, this file allows us to capture and persist every step of +our feature engineering journey into a static file. + +The flow file can then be used to re-create the analysis and feature +engineering steps via Data Wrangler. All you need to do is import the +flow file to SageMaker Studio and click on it. + +We saw previously, how to export transformed dataset into S3. +Additionally, we can also export the analysis and transformations in +many other formats. + +To start exporting, click on the train Dataset data block and click +on the + icon and select **Export to**. + +|image43| + +You can export the analysis and transforms in 4 different ways in +addition to direct export to S3 which we saw previously. + +* Save to S3 as a SageMaker Processing job notebook. +* Export as a SageMaker Pipeline notebook. +* Export as a Python script. +* Export to SageMaker Feature Store as a notebook. + + +So far, we have demonstrated how to use Amazon SageMaker Data Wrangler +to preprocess the data and perform feature engineering to prepare for +the train and test data set. After the data preparation step, data +scientists can work on training a machine learning model using these +datasets. In the next section, we will show you how to directly start a +training job with the train data by leveraging Amazon SageMaker +Autopilot from the SageMaker Data Wrangler data flow. + + +Import Dataflow +---------------------------- + +Here is the final flow file +`Flow file ` __ + available which you can directly +import to expediate the process or validate the flow. + +Here are the steps to import the flow + +* Download the flow file + +* In Sagemaker Studio, drag and drop the flow file or use the upload +button to browse the flow and upload + + + +Optional : Run Autopilot training directly from Data Wrangler flow +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +SageMaker Data Wragler now allow you to directly run an +`Autopilot `__ +job to automatically train a model. + +To set up a SageMaker Autopilot job, click the train data block, +select **Train model**. + +|image44| + +On the new window, select the S3 location you want the training +dataset and the Autopilot job output to be saved. + +|image45| + +Select **Export and train**. This will take about one minute to +export the train data to S3. + +|image46| + +When data exported successfully, we can configure the Autopilot +job. Select the *Target* training column (Rating). + +|image47| + +Under the **Advanced settings**, choose the machine learning +problem type as *Regression*. By default, SageMaker autopilot will +run 250 training jobs to find the best model, this will take a few +hours for the job to finish. To reduce runtime, you can set the +*Max candidates* to a smaller number. + +|image48| + +After click **Create Experiment**, an autopilot job will be +started. You can come back to SageMaker Studio later to check the +job output. + +|image49| + +.. |image| image:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-3.png +.. |image1| image:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-4.png +.. |image2| image:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-5.png +.. |image3| image:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-6.png +.. |image4| image:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-7.png +.. |image5| image:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-1.png +.. |image6| image:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-1-1.png +.. 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https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-52.png diff --git a/sagemaker-datawrangler/joined-dataflow/join.flow b/sagemaker-datawrangler/joined-dataflow/join.flow new file mode 100644 index 0000000000..4e71fe8f05 --- /dev/null +++ b/sagemaker-datawrangler/joined-dataflow/join.flow @@ -0,0 +1,420 @@ +{ + "metadata": { + "version": 1, + "disable_limits": false, + "instance_type": "ml.m5.4xlarge" + }, + "nodes": [ + { + "node_id": "856182f7-a75d-416c-b4d1-f6f06ddb8b89", + "type": "SOURCE", + "operator": "sagemaker.s3_source_0.1", + "parameters": { + "dataset_definition": { + "__typename": "S3CreateDatasetDefinitionOutput", + "datasetSourceType": "S3", + "name": "tracks.csv", + "description": null, + "s3ExecutionContext": { + "__typename": "S3ExecutionContext", + "s3Uri": "s3://sagemaker-sample-files/datasets/tabular/synthetic-music/tracks.csv", + "s3ContentType": "csv", + "s3HasHeader": true, + "s3FieldDelimiter": ",", + "s3DirIncludesNested": false, + "s3AddsFilenameColumn": false + } + } + }, + "inputs": [], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "d25e306e-76b2-4786-8ee8-203f9d5b54f8", + "type": "TRANSFORM", + "operator": "sagemaker.spark.infer_and_cast_type_0.1", + "parameters": {}, + "trained_parameters": { + "schema": { + "trackId": "string", + "length": "float", + "energy": "float", + "acousticness": "float", + "valence": "float", + "speechiness": "float", + "instrumentalness": "float", + "liveness": "float", + "tempo": "float", + "genre": "string" + } + }, + "inputs": [ + { + "name": "df", + "node_id": "856182f7-a75d-416c-b4d1-f6f06ddb8b89", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "9124b18b-f842-48a9-b46d-93bea1476d45", + "type": "SOURCE", + "operator": "sagemaker.s3_source_0.1", + "parameters": { + "dataset_definition": { + "__typename": "S3CreateDatasetDefinitionOutput", + "datasetSourceType": "S3", + "name": "ratings.csv", + "description": null, + "s3ExecutionContext": { + "__typename": "S3ExecutionContext", + "s3Uri": "s3://sagemaker-sample-files/datasets/tabular/synthetic-music/ratings.csv", + "s3ContentType": "csv", + "s3HasHeader": true, + "s3FieldDelimiter": ",", + "s3DirIncludesNested": false, + "s3AddsFilenameColumn": false + } + } + }, + "inputs": [], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "43173440-1736-40e9-805b-8e5547979b02", + "type": "TRANSFORM", + "operator": "sagemaker.spark.infer_and_cast_type_0.1", + "parameters": {}, + "trained_parameters": { + "schema": { + "ratingEventId": "string", + "ts": "long", + "userId": "long", + "trackId": "string", + "sessionId": "long", + "itemInSession": "long", + "Rating": "float" + } + }, + "inputs": [ + { + "name": "df", + "node_id": "9124b18b-f842-48a9-b46d-93bea1476d45", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "be95364d-43ce-4001-a1d5-3d092519eb51", + "type": "TRANSFORM", + "operator": "sagemaker.spark.encode_categorical_0.1", + "parameters": { + "operator": "One-hot encode", + "one_hot_encode_parameters": { + "invalid_handling_strategy": "Keep", + "drop_last": false, + "output_style": "Columns", + "input_column": [ + "genre" + ] + }, + "ordinal_encode_parameters": { + "invalid_handling_strategy": "Replace with NaN" + } + }, + "trained_parameters": { + "one_hot_encode_parameters": { + "_hash": "4623886b68927a4dcb437c2769f9d5234824476a", + "string_indexer_model": 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+ "one_hot_encoder_model": "P)h>@6aWAK2moV6(^Ptj8?@8_003zL000vJ003=ebYWy+bYU-WVRCdWFfcGMFpZB*O9U|thVTB2oM+2SWyamPilE@4C$A;8>F!{^NLvps^WUv23W9={9Ny%4UUCBMFlU${@~j!u0vF5!FemBZSBA&b?O$6w0|*MD~X{fSbacd)J(?@&tv1QY-O00;nMMblJb@Q?-$0000C0000O0001OWprU=VRT_HE^uLTbS*G2FfcGKV{&6lE=p!#U|?d{BHdL508mQ<1QY-O00;nMMblLKRr9Wb0RR9n0ssI=0001FVRT_HaA9(EEif=JFfc7OFf}nUIAu63Wi?_mEi_~>Gc7q`Ff=V=Wn^YFWi>EiF=J*eV=yo}T``_J{Q5qh)0<2`=~JoO{m|x;+0L6GRy4gam`rBCUWnKOuxR)GLVot1ye7SZ3HGq}=>2JQc2EDK)em4F7_+K8dKmd3rM0=|Vj@5!U+d++q_IU^(T@p_c!cQDKL|V39GvM)I&SLUyp&Gl!U_7+STHxANH%2Fnc3K}kf}oI+_K@#f>okimENrZhz;Y7DU1{SyqUM_E!h<@2BJ}ymryJ9=$am3?xcQh+>-j38DSG^>cN{zBi1QY-O00;nMMblKwdm8o<0000G0000@0001FVRT_HE^uLTbS*G2FfcGJH83?XGB{;8EoC)gGc7b^Ff%PVVK6i;V`XG!G-Wj~VKHN7En_e+FfMa$VQ_GHE^uLTadl;ME@N_IOD;-gU|?Wk=Gc7q`Ff=V=Wn^YFWi>EiF=J*eV=yo +Dataset + +The dataset is required before we begin, ensure that you have downloaded it by following the instructions below. + + + +Example track (track.csv) and user ratings (ratings.csv) data is provided on a publicly available S3 bucket found here: **s3://sagemaker-sample-files/datasets/tabular/synthetic-music** +We'll be running a notebook to download the data in the demo so no need to manually download it from here just yet. + +**tracks.csv** + +| **Column name** | **Description** | +| :------------- | :---------- | +|`trackId`| unique identifier for each song/track | +|`length`| song length in seconds (numerical)| +|`energy`| Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity (numerical)| +|`acousticness`| A confidence measure from 0.0 to 1.0 of whether the track is acoustic. (numerical)| +|`valence`| A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. (numerical)| +|`speechiness`| Speechiness detects the presence of spoken words in a track. (numerical)| +|`instrumentalness`| Predicts whether a track contains no vocals. (numerical)| +|`liveness`| Detects the presence of an audience in the recording. (numerical)| +|`tempo`| The overall estimated tempo of a track in beats per minute (BPM). (numerical)| +|`genre`| The genre of the song. For example, pop, rock etc. (categorical) | + +**ratings.csv** + +| **Column name** | **Description** | +| :------------- | :---------- | +|`ratingEventId`| unique identifier for each rating | +|`ts`| timestamp of rating event (datetime in seconds since 1970)| +|`userId`| unique id for each user | +|`trackId`| unique id for each song/track | +|`sessionId`| unique id for the user's session | +|`Rating`| user's rating of song on scale from 1 to 5 | + +For this example, we'll be using our own generated track and user ratings data, but publicly available datasets/apis such as the [Million Song Dataset](http://millionsongdataset.com/) and open-source song ratings APIs are available for personal research purposes. A full end-to-end pipeline can be found in this [SageMaker example](https://github.com/aws/amazon-sagemaker-examples/tree/main/end_to_end/music_recommendation). + +## Pre-requisites: + + * We need to ensure dataset (tracks and ratings dataset) for ML is uploaded to a data source (instructions to download the dataset to Amazon S3 is available in the following section). + * Data source can be any one of the following options: + * S3 + * Athena + * RedShift + * SnowFlake + + +
+Data Source + +For this experiment the Data Source will be [Amazon S3](https://aws.amazon.com/s3/) + +
+ +## Experiment steps + +### Downloading the dataset, and notebooks + +* Ensure that you have a working [Amazon SageMaker Studio](https://aws.amazon.com/sagemaker/studio/) environment and that it has been updated. + +* Follow the steps below to download the dataset. + * Start with the `explore-dataset.ipynb` SageMaker Studio notebook. + * Explore the dataset locally by running the cells in the notebook. + * Upload the datasets (CSV files) to an S3 location for consumption by SageMaker Data Wrangler later. + * Copy the S3 URLs of the tracks and ratings data files to your clipboard. We will use these URLs later to import these part files into Data Wrangler and join them. + * Let's run though each of the steps above. + +* Double click on the file called explore-data.ipynb. Amazon SageMaker may prompt you to select a kernel and image. If it does select Data Science as the image and Python 3 as the Kernel, as shown here: +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-3.png) + +* You have now successfully downloaded the data and opened a notebook, we will now upload the data to your S3 bucket. Note: An Amazon S3 bucket was created for you when the Amazon SageMaker Studio environment was started. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-4.png) + +* From the menu select **Run / Run All Cells** to execute all the cells in the notebook. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-5.png) + +* Examine the results, as you can see the samples of the data are printed to the screen. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-6.png) + +* The data structure is defined in the next section. However, before we continue, note the path to the various datasets. Your paths will be different to the ones in the image below. Please copy these paths as you will use them later. An example of a path is s3://sagemaker-eu-west-1-112233445566/music-recommendation-demo/input/tracks.csv +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/dl-image-7.png) + +
+ Note + + Please also check the S3 location to make sure the files are uploaded successfully before moving to the next section. + +
+ + +In the next section we will import the datasets into Data Wrangler via the SageMaker Studio User Interface (UI). + + +## Importing datasets from a data source (S3) to Data Wrangler + +* Initialize SageMaker Data Wrangler via SageMaker Studio UI. + * There are two ways that you can do this, either from the Launcher screen as depicted here: + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-1.png) + * Or from the SageMaker resources menu on the left, selecting Data Wrangler, and new flow + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-1-1.png) + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-1-2.png) +* It takes a few minutes to load. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-2.png) +* Once Data Wrangler is loaded, you should be able to see it under running instances and apps as shown below. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-3.png) +* Next, make sure you have copied the data paths when running the `explore_data.ipynb` notebook from the previous section (see section: **Downloading the dataset, and notebooks**), as you will need them in this section. +* Once Data Wrangler is up and running, you can see the following data flow interface with options for import, creating data flows and export as shown below. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-4.png) +* Make sure to rename the untitled.flow to your preference (for e.g., join.flow) +* Paste the S3 URL for the tracks.csv file into the search box below and hit go. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-5.png) +* Select the CSV file from the drop down results. On the right pane, make sure COMMA is chosen as the delimiter and Sampling is *None*. Hit *import* to import this dataset to Data Wrangler. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-6.png) +* Once the dataset is imported, the Data flow interface looks as shown below. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-7.png) +* Since currently you are in the data flow tab, hit the import tab (left of data flow tab) as seen in the above image. +* Import the second part file (ratings.csv) following the same set of instructions as noted previously. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-8.png) + +### Transform tracks dataset +* We firstly want to perform some data transformation using Data Wrangler. Let us walkthough how to perform different transformations using built-in and custom formula functionality in Data Wrangler. + * As the *genre* column in the tracks dataset is a categorical feature, we need to perform one-hot encoding to trasform this feature. + * Click on the tracks file transform block as show in the image below and select **Add transform**: + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-9.png) + * This takes us to the Data Wrangler transformations interface where there are over 300+ transformations you can apply to your dataset. Select **Add step** as shown below. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-10.png) + * In the **ADD TRANFORM** window, double click the option **Encode categorical**. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-11.png) + * Then on the **ENCODE CATEGORICAL** window, choose *One-hot encode* as the Transform type, *genre* as the input columns, and *Columns* as the output style. Click *Preview* and the output is shown as below: + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-12.png) + * Click **Add** to add the tranform step to the flow. If you go back to the *Data Flow*, you can see the step has been added. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-13.png) + +* We also want to generate a new feature based on the danceability of the track. Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. + * Click on the newly added **One-hot encode** step and select *Add transformation*: + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-14.png) + * Select **Add step** and choose **Custom formula**. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-15.png) + * Copy and paste below formula and put **danceability** to the *Output Column*. + + 0.3\*valence + 0.1\*liveness + 0.1\*energy + + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-16.png) + + * Click **Preview** and **Add** the step to the flow. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-17.png) + +### Joining datasets - first join +* Given, we have imported both the tracks and ratings CSV files in the beginning steps. Let us walk through on how to join these CSV files based on a common unique identifier column, *trackId*. Then we will perform some feature engineering to generate a new set of features that can help to enrich the trainig data. + +* Click on either the tracks or ratings transform block as shown in the image blow: + * Here, we have selected tracks transform flow block and hit **Join** + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-18.png) + * Select the other rating file transform block and it automatically maps (converges) both the files into a Join preview as shown below. +
+ Note + + Files can also be concatenated similar to join operations + +
+ + * Hit configure. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-19.png) + * Here, choose a name for the resulting join file and choose the type of join and columns on to join (Please refer to the image below). + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-20.png) + * Hit **Apply** (*Preview*) . You can see a preview of the Joined dataset as shown in the image below. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-21.png) +
+ Note + + Depending on the version of SageMaker it might be Preview and not Add + +
+ + * Hit *Add* at the upper right corner to add this Join transform to the original data flow. + * At the end of this step, the data flow looks as shown below. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-22.png) + + * Next step, let's see how to use Data Wrangler to add custom transform to perform more advanced feature engineering. Here, we want to use pyspark to calculate the average values of 5-star ratings for different columns and use them as new features. + * Select on the jointrackid.csv block and click the + icon, under which click on Add transform. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-23.png) + * Click the custom transform at this step. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-24.png) + * Give a name to this custom transform step and copy and paste below custom script to the window. + +```python +# Table is available as variable `df` +import pyspark.sql.functions as F +# numeric features +num_feat_cols = ['energy', 'acousticness', 'valence', 'speechiness', 'instrumentalness', 'liveness', 'tempo', 'danceability', 'genre_Latin', 'genre_Folk', 'genre_Blues', 'genre_Rap', 'genre_Reggae', 'genre_Jazz', 'genre_RnB', 'genre_Country', 'genre_Electronic', 'genre_Pop_Rock'] +# calculate averages for 5-star ratings +agg_obj = [F.avg(feat_col).alias(feat_col+"_5star") for feat_col in num_feat_cols] +df = df.filter(F.col('Rating')==5).groupBy('userId').agg(*agg_obj) +``` +* + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-25.png) + * Click *Preview* and the *Add* this step to the flow. + +* We want to join the generated new features back to the original data. Before joining back, we notice some columns in the joint dataset are not needed for the model training, such as the id related columns. Let’s see now how to add a simple transform using Data Wrangler to drop the columns after the JOIN operation we did previously. + * Select the jointrackid.csv block and select *Add transform*. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-26.png) + * Let us apply the manage columns transform to drop some columns listed as below + * *trackId_0* + * *trackId_1* + * *ts* + * *sessionId* + * *itemInSession* + * *ratingEventId* + + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-27.png) + + * we can drop multiple columns by selecting each column from the drop down manual. + + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-28.png) + + * Once all the columns are selected, hit **Preview** first and then **Add**. + + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-29.png) + + * Now go back to data flow. + * You should now be able to see the 2 transforms (custom transform and dropping the columns) as shown below in the Data Flow interface. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-30.png) + + * Next step is to join the two dataset back together. Similarly as the first join, we select one block and choose *Join*. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-31.png) + * Select the other file transform block and get a Join preview. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-32.png) + * Fill in the step Name, Join Type and columns to join on (*UserId*). + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-33.png) + * Preview and Add this step to the flow file. When we go back to the data flow, this is how the flow looks like now. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-34.png) + * After joining the two data source, we also want to drop the *userId* columns and move the target column *Rating* to the first column. + * Similar to the previous manage columns transform instructions, we add two transform steps to drop the *userId_0* and *userId_1* columns, and then move the *Rating* step to the start of the table. + + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-35.png) + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-36.png) + +* Once all the transform steps are finished, we will export the transformed data. SageMaker Data Wrangler also allow you to split your dataset into train and test based on the ratio you set. + * To split the dataset, add another transform step and choose **Split data**. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-37.png) + * We choose *Randomized split* and get 80% for training and 20% data for testing. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-38.png) + * The data flow now looks as below: + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-39.png) + + +## Export transformed features to S3 (will be consumed by SageMaker Autopilot) + +* To export the transformed dataset, first click on the + symbol and choose Add Destination, followed by Amazon S3 as pointed out by the screen shot below. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-40.png) + +* A new window is opened, Click Export data, choose the S3 location where you want to save the transformed dataset. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-41.png) +* Follow the same step to set the S3 location for the test data. + +* A job is needed to export the data to Amazon S3, to do this press the Create Job button on the top right, this will open a window. + +* Set the Job name to something like generate-train-test-data + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-42.png) + +* Hit the **Configure Job** button at the bottom + +* Leave the default instance type, and press the Run button at the bottom. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-43.png) + +* Note that your job has been created successfully and if you want to see the progress of the job you can do so by following the link to the generate-train-test-data process. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-44.png) + +* Follow the link to see the status of your job. This processing job takes around 5-10 mins. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-45.png) + +* When the job is complete the train and test output files will be available in the corresponding S3 output folders. You can find the output location from the processing job configurations. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-processing-job-output.png) + +### *Other ways to export the transformations and analysis* + +* The join.flow file that we created initially captures all of the transformations, joins and analysis. +* In a way, this file allows us to capture and persist every step of our feature engineering journey into a static file. +* The flow file can then be used to re-create the analysis and feature engineering steps via Data Wrangler. All you need to do is import the flow file to SageMaker Studio and click on it. +* We saw previously, how to export transformed dataset into S3. Additionally, we can also export the analysis and transformations in many other formats. +* To start exporting, click on the train Dataset data block and click on the + icon and select **Export to**. +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-46.png) +* You can export the analysis and transforms in 4 different ways in addition to direct export to S3 which we saw previously. + * Save to S3 as a SageMaker Processing job notebook. + * Export as a SageMaker Pipeline notebook. + * Export as a Python script. + * Export to SageMaker Feature Store as a notebook. + +:bulb:**NOTE** - Also, you can import the [flow file](./join.flow) by following the steps [here](../import-flow.md) + +So far, we have demonstrated how to use Amazon SageMaker Data Wrangler to preprocess the data and perform feature engineering to prepare for the train and test data set. After the data preparation step, data scientists can work on training a machine learning model using these datasets. In the next section, we will show you how to directly start a training job with the train data by leveraging Amazon SageMaker Autopilot from the SageMaker Data Wrangler data flow. + +### *Run Autopilot training directly from Data Wrangler flow* (**Optional**) +* SageMaker Data Wragler now allow you to directly run an [Autopilot](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html) job to automatically train a model. + * To set up a SageMaker Autopilot job, click the train data block, select **Train model**. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-47.png) + * On the new window, select the S3 location you want the training dataset and the Autopilot job output to be saved. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-48.png) + * Select **Export and train**. This will take about one minute to export the train data to S3. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-49.png) + * When data exported successfully, we can configure the Autopilot job. Select the *Target* training column (Rating). + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-50.png) + * Under the **Advanced settings**, choose the machine learning problem type as *Regression*. By default, SageMaker autopilot will run 250 training jobs to find the best model, this will take a few hours for the job to finish. To reduce runtime, you can set the *Max candidates* to a smaller number. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-51.png) + * After click **Create Experiment**, an autopilot job will be started. You can come back to SageMaker Studio later to check the job output. + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/joined-dataflow/image-52.png) diff --git a/sagemaker-datawrangler/readme.md b/sagemaker-datawrangler/readme.md new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/sagemaker-datawrangler/readme.md @@ -0,0 +1 @@ + diff --git a/sagemaker-datawrangler/tabular-dataflow/Hotel-Bookings-Classification.flow b/sagemaker-datawrangler/tabular-dataflow/Hotel-Bookings-Classification.flow new file mode 100644 index 0000000000..f6eb64de42 --- /dev/null +++ b/sagemaker-datawrangler/tabular-dataflow/Hotel-Bookings-Classification.flow @@ -0,0 +1,242231 @@ +{ + "metadata": { + "version": 1, + "disable_limits": false, + "instance_type": "ml.m5.4xlarge" + }, + "nodes": [ + { + "node_id": "1f7fb244-77d9-4d90-9dba-8d9956ba5f79", + "type": "SOURCE", + "operator": "sagemaker.s3_source_0.1", + "parameters": { + "dataset_definition": { + "__typename": "S3CreateDatasetDefinitionOutput", + "datasetSourceType": "S3", + "name": "hotel_bookings.csv", + "description": null, + "s3ExecutionContext": { + "__typename": "S3ExecutionContext", + "s3Uri": "s3://hotelbookings/hotel_bookings.csv", + "s3ContentType": "csv", + "s3HasHeader": true, + "s3FieldDelimiter": ",", + "s3DirIncludesNested": false, + "s3AddsFilenameColumn": false + } + } + }, + "inputs": [], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "9ed99534-7891-44ba-972c-849614c96e20", + "type": "TRANSFORM", + "operator": "sagemaker.spark.infer_and_cast_type_0.1", + "parameters": {}, + "trained_parameters": { + "schema": { + "hotel": "string", + "is_canceled": "long", + "lead_time": "long", + "arrival_date_year": "long", + "arrival_date_month": "string", + "arrival_date_week_number": "long", + "arrival_date_day_of_month": "long", + "stays_in_weekend_nights": "long", + "stays_in_week_nights": "long", + "adults": "long", + "children": "long", + "babies": "long", + "meal": "string", + "country": "string", + "market_segment": "string", + "distribution_channel": "string", + "is_repeated_guest": "long", + "previous_cancellations": "long", + "previous_bookings_not_canceled": "long", + "reserved_room_type": "string", + "assigned_room_type": "string", + "booking_changes": "long", + "deposit_type": "string", + "agent": "long", + "company": "string", + "days_in_waiting_list": "long", + "customer_type": "string", + "adr": "float", + "required_car_parking_spaces": "long", + "total_of_special_requests": "long", + "reservation_status": "string", + "reservation_status_date": "date" + } + }, + "inputs": [ + { + "name": "default", + "node_id": "1f7fb244-77d9-4d90-9dba-8d9956ba5f79", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "fd325a95-b061-46bb-a9bc-f0d4b1b10b85", + "type": "VISUALIZATION", + "operator": "sagemaker.visualizations.data_insights_report_0.1", + "parameters": { + "report_content": "Warning summary", + "insights_report_parameters": { + "target_column": "is_canceled", + "problem_type": "Classification" + } + }, + "trained_parameters": { + "insights_report_parameters": { + "duplicate_ratio": 0.3364184605075802, + "high warnings": [ + { + "title": "Duplicate rows", + "warningText": "

We found that 33.6% of the data are duplicate. Some data sources could include valid duplicates and in other cases these duplicates could point to problems in data collection. Duplicate samples resulting from faulty data collection, could derail machine learning processes that rely on splitting to independent training and validation folds. For example quick model scores, prediction power estimation and automatic hyper parameter tuning. Duplicate samples could be removed from the dataset using the Drop duplicates transform under Manage rows.

", + "severity": "high_sev" + }, + { + "title": "Target leakage", + "warningText": "

The feature reservation_status predicts the target extremely well on it's own. A feature this predictive often indicates an error called target leakage. The cause is typically data that is not available at time of prediction. For example, a duplicate of the target column in the dataset can result in target leakage.
Alternatively, if the machine learning task is \"easy\", then a single feature can have legitimately high prediction power. If you think that a single feature is very highly predictive, you don't need to do anything further. However, if you think there's target leakage, we recommended that you remove the highly predictive column from the dataset using the Drop column transform under Manage columns.

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"stays_in_weekend_nights", + "stays_in_week_nights", + "is_repeated_guest", + "previous_cancellations", + "previous_bookings_not_canceled", + "booking_changes", + "adr", + "total_of_special_requests", + "required_car_parking_spaces" + ] + }, + "standard_scaler_parameters": { + "scale": true + } + } + }, + "inputs": [ + { + "name": "df", + "node_id": "fb157a48-c80d-41a7-bdaf-7062d5ef5723", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "5a1bb436-3fed-4e90-8fee-4b54bfc086f2", + "type": "TRANSFORM", + "operator": "sagemaker.spark.encode_categorical_0.1", + "parameters": { + "operator": "One-hot encode", + "one_hot_encode_parameters": { + "invalid_handling_strategy": "Keep", + "drop_last": false, + "output_style": "Columns", + "input_column": [ + "meal", + "is_repeated_guest", + "market_segment", + "assigned_room_type", + "deposit_type", + "customer_type" + ] + }, + "ordinal_encode_parameters": { + "invalid_handling_strategy": "Replace with NaN" + } + }, + "inputs": [ + { + "name": "df", + "node_id": "8114e62b-f341-4713-af7b-5af022ff768f", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "db7ff0f7-6ef8-4d7e-a676-359fe6574514", + "type": "TRANSFORM", + "operator": "sagemaker.spark.balance_data_0.1", + "parameters": { + "operator": "Random oversample", + "ratio": 1, + "smote_params": { + "num_neighbors": 5 + }, + "target_column": "is_canceled" + }, + "inputs": [ + { + "name": "df", + "node_id": "5a1bb436-3fed-4e90-8fee-4b54bfc086f2", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "89e29e9a-61ab-4cf0-9516-f723cfaf85c8", + "type": "VISUALIZATION", + "operator": "sagemaker.visualizations.quick_model_0.1", + "parameters": { + "name": "Model (Post Transform)", + "label": "is_canceled" + }, + "inputs": [ + { + "name": "df", + "node_id": "db7ff0f7-6ef8-4d7e-a676-359fe6574514", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "8c6c0988-f626-44a0-bc84-195de676cfd1", + "type": "DESTINATION", + "operator": "sagemaker.spark.s3_destination_0.1", + "name": "S3: Hotel-Bookings-Dataset", + "parameters": { + "output_config": { + "compression": "none", + "output_path": "s3://mlopsbucket12345/", + "output_content_type": "CSV", + "delimiter": "," + } + }, + "inputs": [ + { + "name": "default", + "node_id": "db7ff0f7-6ef8-4d7e-a676-359fe6574514", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + } + ] +} \ No newline at end of file diff --git a/sagemaker-datawrangler/tabular-dataflow/README.md b/sagemaker-datawrangler/tabular-dataflow/README.md new file mode 100644 index 0000000000..a5dd7b2429 --- /dev/null +++ b/sagemaker-datawrangler/tabular-dataflow/README.md @@ -0,0 +1,126 @@ +# Hotel Booking Demand Example + + + +## Background + +Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy machine learning models quickly by bringing together a broad set of purpose-built capabilities. This example shows how SageMaker can accelerate machine learning development during the data preprocessing stage to help process the hotel demand data and find relevant features for the training model to predict hotel cancellations. + +### Dataset + +
+Dataset + +We will be using the [Hotel Booking Demand dataset](https://www.kaggle.com/jessemostipak/hotel-booking-demand) that is publically available. This data set contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things.

+ +The data needs to be downloaded from the locations specified, and uploaded to S3 bucket before we start the Data Preprocessing phase. Please follow the Experiment Steps outlined in later sections, to download the data and notebooks. + + +## Description of the Columns + + + +| Column Name | Description | +|---|---| +| `hotel` | Type of the hotel (`H1` = Resort Hotel or `H2` = City Hotel) | +| `is_canceled` | Value indicating if the booking was canceled (1) or not (0) | +| `lead_time` | Number of days that elapsed between the entering date of the booking into the PMS and the arrival date | +| `arrival_date_year` | Year of arrival date | +| `arrival_date_month` | Month of arrival date | +| `arrival_date_week_number` | Week number of year for arrival date | +| `arrival_date_day_of_month` | Day of arrival date | +| `stays_in_weekend_nights` | Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel | +| `stays_in_week_nights` | Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel | +| `adults` | Number of adults | +| `children` | Number of children | +| `babies` | Number of babies | +| `meal` | Type of meal booked. Categories are presented in standard hospitality meal packages: `Undefined/SC` – no meal package; `BB` – Bed & Breakfast; `HB` – Half board (breakfast and one other meal – usually dinner); `FB` – Full board (breakfast, lunch and dinner) | +| `country`| Country of origin. Categories are represented in the `ISO 3155–3:2013` format | +|`market_segment`|Market segment designation. In categories, the term `TA` means “Travel Agents” and `TO` means “Tour Operators”| +|`distribution_channel`|Booking distribution channel. The term `TA` means “Travel Agents” and `TO` means “Tour Operators”| +|`is_repeated_guest`|Value indicating if the booking name was from a repeated guest (1) or not (0)| +|`previous_cancellations`|Number of previous bookings that were cancelled by the customer prior to the current booking| +|`previous_bookings_not_canceled`|Number of previous bookings not cancelled by the customer prior to the current booking| +|`reserved_room_type`|Code of room type reserved. Code is presented instead of designation for anonymity reasons.| +|`assigned_room_type`|Code for the type of room assigned to the booking. Sometimes the assigned room type differs from the reserved room type due to hotel operation reasons (e.g. overbooking) or by customer request. Code is presented instead of designation for anonymity reasons.| +|`booking_changes`|Number of changes/amendments made to the booking from the moment the booking was entered on the PMS until the moment of check-in or cancellation| +|`deposit_type`|Indication on if the customer made a deposit to guarantee the booking. This variable can assume three categories: No Deposit – no deposit was made; `Non Refund` – a deposit was made in the value of the total stay cost; `Refundable` – a deposit was made with a value under the total cost of stay.| +|`agent`|ID of the travel agency that made the booking| +|`company`|ID of the company/entity that made the booking or responsible for paying the booking. ID is presented instead of designation for anonymity reasons| +|`days_in_waiting_list`|Number of days the booking was in the waiting list before it was confirmed to the customer| +|`customer_type`|Type of booking, assuming one of four categories: `Contract` - when the booking has an allotment or other type of contract associated to it; `Group` – when the booking is associated to a group; `Transient` – when the booking is not part of a group or contract, and is not associated to other transient booking; `Transient-party` – when the booking is transient, but is associated to at least other transient booking| +|`adr`|Average Daily Rate as defined by dividing the sum of all lodging transactions by the total number of staying nights| +|`required_car_parking_spaces`|Number of car parking spaces required by the customer| +|`total_of_special_requests`|Number of special requests made by the customer (e.g. twin bed or high floor)| +|`reservation_status`|Reservation last status, assuming one of three categories: `Canceled` – booking was canceled by the customer; `Check-Out` – customer has checked in but already departed; `No-Show` – customer did not check-in and did inform the hotel of the reason why| +|`reservation_status_date`|Date at which the last status was set. This variable can be used in conjunction with the ReservationStatus to understand when was the booking canceled or when did the customer checked-out of the hotel| + +--- + +## Pre-requisites: + + * We need to ensure dataset (tracks and ratings dataset) for ML is uploaded to a data source (instructions to download the dataset to Amazon S3 is available in the following section). + * Data source can be any one of the following options: + * S3 + * Athena + * RedShift + * SnowFlake + + +
+Data Source + +For this experiment the Data Source will be [Amazon S3](https://aws.amazon.com/s3/) + +
+ +## Experiment steps + +### Downloading the dataset + +* Ensure that you have a working [Amazon SageMaker Studio](https://aws.amazon.com/sagemaker/studio/) environment and that it has been updated. + +* Follow the steps below to download the dataset. +1. Download the [Hotel Booking Demand dataset](https://www.kaggle.com/jessemostipak/hotel-booking-demand) from the specified location. +2. Create a private S3 bucket to upload the dataset in. You can reference the instructions for bucket creaiton [here] https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html +3. Upload the data in step 1 to the bucket created in step 2. Steps to upload the data can be found [here] https://docs.aws.amazon.com/AmazonS3/latest/userguide/upload-objects.html +4. Note the S3 URL for the file uploaded in Step 3 before moving to the next sextion. This data will be used as input to the Datawrangler. The S3 URL will be used in the next step. + +### Data Import from S3 to Data Wrangler +The hotel-bookings.csv file uploaded in previous section needs to be imported in Data Wrangler as input. Please refer to **[Data Import from S3](data-import/Data-Import.md)** and follow steps for importing the data. + +### Exploratory Data Analysis +Before applying various data transformations, we need to explore the data to find correlations, duplicate rows as well as target leakage. Please refer to **[Exploratory Data Analysis](data-exploraion/Data-Exploration.md)** and follow steps for Data exploration. + +### Data Transformation +Based on the Data explorations carried out in previous step, we are now ready to apply transformations to the data. Please refer to **[Data Transformations](data-transformation/Data-Transformations.md)** and follow steps for Data Transformation. + +### Data Export +Data Wrangler UI can also be used to export the transformed data to Amazon S3. To get started with this process, first let's create a destination node. Right click on the final transform on your data and choose `Add destination` → `Amazon S3`. Assign a name for your output data and choose the S3 location where you want the data to be stored and click Add destination button at the bottom as shown below. + +![add-destination](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/add-destination.png) + +This adds a destination node to our data flow. The destination node acts as a sink to your data flow. + +![destination](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/destination.png) + +Next, click on the Create job button in the upper right corner of the page. In the configuration page for the SageMaker Processing job we are about to create, choose the Instance type and Instance count for our processing cluster. Advance configuration is optional, where you can assign tags as needed and choose the appropriate Volume Size. + +![export-create-job](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/configure-job.png) + +Select `Run` to start the export job. The job created will have Job Name and Job ARN which can be used to search for the job status. + +![export-create-job](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/create-job.png) + +The job created in the previous step will be available in the monitoring page for `SageMaker Processing job` as shown in the figure below. + +![export-processing-job](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/processing-job.png) + +After a certain time, the job will be complete. The image below shows the completed job. Exported data should be available in the output S3 bucket. + +![export-complete-job](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/complete-job.png) + + + This exported data can now be used for running the ML Models + + :bulb:**NOTE** - Also, you can import the [flow file](./Hotel-Bookings-Classification.flow) by following the steps [here](../import-flow.md) diff --git a/sagemaker-datawrangler/tabular-dataflow/data-exploration/Data-Exploration.md b/sagemaker-datawrangler/tabular-dataflow/data-exploration/Data-Exploration.md new file mode 100644 index 0000000000..4b4d6cec78 --- /dev/null +++ b/sagemaker-datawrangler/tabular-dataflow/data-exploration/Data-Exploration.md @@ -0,0 +1,242 @@ +## Exploratory Data Analysis + +### Analyze and Visualize + +Before we transform our raw features to make it ML ready for model building, lets analyze and visualize the booking cancellations dataset to detect features that are important to our problem and ones that are not. This can be achieved through Exploratory Data Analysis (EDA). Amazon SageMaker Data Wrangler includes built-in analyses that help you generate visualizations and data analyses in a few clicks. You can also create custom analyses using your own code. + +In order to apply an action on the imported data, select the **Add Analysis** option on right clicking the the **Data Types** block. As depicted in the figure below, you can see options to add a transform, perform an analysis, add a destination sink or export the steps as Jupyter notebook. You can also join and concatenate on the imported dataset with other datasets. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-8.png) + +On selecting Add Analysis, the Analysis pane os shown, where you can choose the analysis you want to perform. + +All analyses are generated using 100,000 rows of your dataset. + +You can add the following analysis to a dataframe: + +* Data visualizations, including histograms and scatter plots. +* A quick summary of your dataset, including number of entries, minimum and maximum values (for numeric data), and most and least frequent categories (for categorical data). +* A quick model of the dataset, which can be used to generate an importance score for each feature. +* A target leakage report, which you can use to determine if one or more features are strongly correlated with your target feature. +* A custom visualization using your own code. + + +Following sections showcase few of the analysis techniques for the Hotel-bookings data. + +### Get Insights + +You can get the Data Insights report by selecting **Get Insights** option for the **Data Types** block as shown in the figure. + + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/get-insights.png) + +Please select the following parameters and hit **Create**. + - `Target column`: `is-cancelled` + - `Problem type`: `Classification` + +After the report is generated, it outlines findings about statistics, duplicate rows, warnings, confusion matrix and feature summary. This can be a useful report before we start our detailed analysis. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/get-insights-report.png) + + + +### Table Summary + +Select **Table Summary** Analysis in the **Add Analysis** window. + +Please select the following parameters and hit **Preview**. +- `Analysis name`: `Table Summary` + +Overall details of the data for various columsn is displayed as depicted in figure below. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/table-information.png) + +Select **Save** to save the analysis. + + +### Target Leakage + +Target leakage occurs when there is data in a machine learning training dataset that is strongly correlated with the target label, but is not available in real-world data. For example, you may have a column in your dataset that serves as a proxy for the column you want to predict with your model. + +When you use the Target Leakage analysis, you specify the following: +* **Target**: This is the feature about which you want your ML model to be able to make predictions. +* **Problem type**: This is the ML problem type on which you are working. Problem type can either be classification or regression. +* (Optional) **Max features**: This is the maximum number of features to present in the visualization, which shows features ranked by their risk of being target leakage. + +For classification, the target leakage analysis uses the area under the receiver operating characteristic, or AUC - ROC curve for each column, up to Max features. For regression, it uses a coefficient of determination, or R2 metric. + +The AUC - ROC curve provides a predictive metric, computed individually for each column using cross-validation, on a sample of up to around 1000 rows. A score of 1 indicates perfect predictive abilities, which often indicates target leakage. A score of 0.5 or lower indicates that the information on the column could not provide, on its own, any useful information towards predicting the target. Although it can happen that a column is uninformative on its own but is useful in predicting the target when used in tandem with other features, a low score could indicate the feature is redundant. + +To create a target leakage analysis, Select **Target Leakage** Analysis in the **Add Analysis** window. +Please select the following parameters and hit **Preview**. +- `Analysis name`: `Target Leakage` +- `Max features` : `40` +- `Target column`: `is-cancelled` +- `Problem type`: `Classification` + + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/target-leakage-pre.png) + + +For our example dataset, the image below shows a target leakage report for a hotel booking cancellation problem, that is, predicting if a person will cancel his hotel reservation or not. An AUC - ROC curve is used to calculate the predictive ability of 31 raw features, out of which `reservation_status` was determined to a target leakage. Also, features - `arrival_day_of_month`, `babies`, `reservation_status_date`, `arrival_date_month`, `reserved_room_type`, `hotel` and `days_in_waiting_list` were identified as redundant. + +The identified features can be fairly omitted as part of the transformations we will apply post this initial analysis. + +![target-leakage](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/target-leakage.png) + +Select **Save** to save the analysis. + + + + +Next, with SageMaker Data Wrangler’s feature correlation visualization you can easily calculate the correlation of features in your data set and visualize them as a correlation matrix. We will look into 2 types of feature correlations and how to use them on our example dataset in hand. + +### Feature Correlation (Linear) + +Linear feature correlation is based on Pearson's correlation. Numeric to categorical correlation is calculated by encoding the categorical features as the floating point numbers that best predict the numeric feature before calculating Pearson's correlation. Linear categorical to categorical correlation is not supported. + +Numeric to numeric correlation is in the range [-1, 1] where 0 implies no correlation, 1 implies perfect correlation and -1 implies perfect inverse correlation. Numeric to categorical and categorical to categrical correlations are in the range [0, 1] where 0 implies no correlation and 1 implies perfect correlation. +To create the analysis, choose **Feature Correlation** for the Analysis type and choose **linear** for Correlation type. Please select the following parameters and hit **Preview**. +- `Analysis name`: `Linear Correlation` + +This analysis will take a few minutes to complete. + +Features that are not either numeric or categorical are ignored. The table below lists for each feature what is the most correlated feature to it. + +![linear-pre](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/linear-pre.png) + +Based on the correlation values, we can see the top 6 feature pairs (as listed below) are strongly correlating with one another. Also, some of these features also showed up in the target analysis we did previously. + +![linear-correlated](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/linear-strongly-correlated.png) + +P.S.: A limit of 100,000 rows is used for this analysis. + +Select **Save** to save the analysis. + +### Feature Correlation (Non-Linear) + +Non-linear feature correlation is based on Spearman's rank correlation. Numeric to categorical correlation is calculated by encoding the categorical features as the floating point numbers that best predict the numeric feature before calculating Spearman's rank correlation. Categorical to categorical correlation is based on the normalized Cramer's V test. + +Numeric to numeric correlation is in the range [-1, 1] where 0 implies no correlation, 1 implies perfect correlation and -1 implies perfect inverse correlation. Numeric to categorical and categorical to categrical correlations are in the range [0, 1] where 0 implies no correlation and 1 implies perfect correlation + +Features that are not either numeric or categorical are ignored. + +To create the analysis, choose **Feature Correlation** for the Analysis type and **non-linear** for Correlation type. Please select the following parameters and hit **Preview**. +- `Analysis name`: `Non-Linear Correlation` + +This analysis will take a few minutes to complete. + +The table below lists for each feature what is the most correlated feature to it. You can see most of the top correlated feature pairs overlap with the previous two analyses. + +![non-linear-correlated](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/non-linear-pre.png) + +Select **Save** to save the analysis. + +### Multicolinearity (Variance Inflation Factors) + +Variance Inflation Factor (VIF) is a measure of colinearity among variables. It is calculated by solving a regression problem to predict one variable given the rest. A VIF score is a positive number that is greater or equal than 1, and a score of 1 means the variable is completely independent of the others. The larger the score, the more dependent it is. Since it is an inverse, it is possible for the VIF score to be infinite. Note that we cap the VIF score at 50. As a rule of thumb for cases where the number of samples is not abnormally small, a score of up to 5 means the variable is only moderatly correlated, and beyond 5 it is highly correlated. + +To create the analysis for VIF, choose **Multicollinearity** for Analysis type and choose **Variance inflation factors** for Analysis. Please select the following parameters and hit **Preview**. +- `Analysis name`: `Variance Inflation Factors` + +This analysis will take a few minutes to complete. + +As per the above rule, we can eliminate the following feature columns from our feature set since they will not contribute effectively towards the prediction capability of the model that gets trained using these features. + +* `arrival_date_year` +* `adults`, `agents` +* `arrival_date_week_number` +* `stays_in_week_nights` + +![variance-inflation-factors](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/vif-pre.png) + +Select **Save** to save the analysis. + + +### Multicolinearity - Principal Component Analysis (PCA) + +Principal Component Analysis (PCA) measures the variance of the data along different directions in the feature space. The ordered list of variances, also known as the singular values, can inform about multicolinearity in our data. This list contains non-negative numbers. When the numbers are roughly uniform, the data has very few multicolinearities. However, when the opposite is true, the magnitude of the top values will dominate the rest. In order to avoid issues related to different scales, the individual features are standardized to have mean 0 and standard deviation 1 before applying PCA. + +To create the analysis for PCA, choose **Multicollinearity** for Analysis type and choose **Principal component analysis** for Analysis. Please select the following parameters and hit **Preview**. +- `Analysis name`: `Principal Component Analysis` + +This analysis will take a few minutes to complete. + +As per the above rule, it is evident the numbers (variances) are not uniform hence confirming that the data has multicolinearies to fix. This has already been confirmed by our previous analysis. + + + +![pca-pre](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/pca-pre.png) + +Select **Save** to save the analysis. + + +### Multicolinearity Lasso Feature Selection + + +Lasso feature selection trains a linear classifier with L1 regularization (you can control the strength of L1 penalty by adjusting "L1 magnitude") that induces a sparse solution. The regressor provides a coefficient for each feature, and the absolute value of this coefficient could be interpreted as an importance score for that feature. + +To create the analysis for Lasso Feature Selection, choose **Multicollinearity** for Analysis type and choose **Lasso feature selection** for Analysis. Please select the following parameters and hit **Preview**. +- `Analysis name`: `Non-Linear Correlation` +- `L1 Magnitude`: `1` +- `Problem Type`: `Classification` +- `Label Column` : `is_cancelled` + +This analysis will take a few minutes to complete. + + +The plot below provides features' importance scores (absolute coefficients) after training a classifier on a sample of the dataset (10k for large dataset). The training process includes a standardization of the features to have mean 0 and standard deviation 1 in order to avoid a skewed importance score due to different scales. + +The classifier obtained a roc_auc score: `0.639269142214666`. + +![lasso-pre](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/lasso-pre.png) + +Select **Save** to save the analysis. + + + +### Detect Duplicate Rows +Next, with the new duplicate row detection visualization, you can quickly detect if your data set has any duplicate rows. To apply this analysis, choose **Duplicate rows** for Analysis type. + +From the figure bwlow, we can see almost ~33% of the rows in the dataset are duplicates. + +![duplicate](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/duplicate-rows.png) + + + +### Quick Model + +We can create a quick model using the raw features to determine how good our features are, prior to applying transformations. + +Use the Quick Model visualization to quickly evaluate your data and produce importance scores for each feature. A feature importance score indicates how useful a feature is at predicting a target label. The feature importance score is between [0, 1] and a higher number indicates that the feature is more important to the whole dataset. On the top of the quick model chart, there is a model score. A classification problem shows an F1 score. A regression problem has a mean squared error (MSE) score. + +When you create a quick model chart, you select a dataset you want evaluated, and a target label against which you want feature importance to be compared. Data Wrangler does the following: +* Infers the data types for the target label and each feature in the dataset selected. +* Determines the problem type. Based on the number of distinct values in the label column, Data Wrangler determines if this is a regression or classification problem type. Data Wrangler sets a categorical threshold to 100. If there are more than 100 distinct values in the label column, Data Wrangler classifies it as a regression problem; otherwise, it is classified as a classification problem. +* Pre-processes features and label data for training. The algorithm used requires encoding features to vector type and encoding labels to double type. +* Trains a random forest algorithm with 70% of data. Spark’s RandomForestRegressor is used to train a model for regression problems. The RandomForestClassifier is used to train a model for classification problems. +* Evaluates a random forest model with the remaining 30% of data. Data Wrangler evaluates classification models using an F1 score and evaluates regression models using an MSE (mean squared error) score. +* Calculates feature importance for each feature using the Gini importance method. + +Let us create a prediction model on the fly for the problem for the booking cancellation problem using the raw crude features we started with in Data Wrangler's Quick Model option. + +Please choose **Quick Model** for Analysis type. Select the following parameters and hit **Preview**. +- `Analysis name`: `Model pre-transform` + +This analysis will take a few minutes to complete. + +![quick-model-pre](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/create-quick-model.png) + +A limit of 100,000 rows is used for this analysis. You can use the Quick Model feature to provide a rough estimate of the expected predicted quality and the predictive power of the features in your dataset. + +We can from the results below, Quick model was able to predict with an F1 score of 82% on the test set. But, this is misleading, given we haven't eliminated most of the feature columns that are a target leakage or redundant based on high colinearity. This is justified in the results below where the column `reservation_status` which is a target leakage ranked as the most important feature. + + +![quick-model-pre](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/quick-model-pre.png) + +Select **Save** to save the model. + +### Next Steps + +In the next section, we will apply post analysis transformations to fix the data of the various colinearity and other issues and re-generate a quick model and compare the differences. Please refer to **[Data Transformations](./Data-Transformations.md)** and follow steps for Data Transformation. + diff --git a/sagemaker-datawrangler/tabular-dataflow/data-exploration/index.rst b/sagemaker-datawrangler/tabular-dataflow/data-exploration/index.rst new file mode 100644 index 0000000000..e3dc6e001d --- /dev/null +++ b/sagemaker-datawrangler/tabular-dataflow/data-exploration/index.rst @@ -0,0 +1,408 @@ +Exploratory Data Analysis +========================= + +Analyze and Visualize +--------------------- + +Before we transform our raw features to make it ML ready for model +building, lets analyze and visualize the booking cancellations dataset +to detect features that are important to our problem and ones that are +not. This can be achieved through Exploratory Data Analysis (EDA). +Amazon SageMaker Data Wrangler includes built-in analyses that help you +generate visualizations and data analyses in a few clicks. You can also +create custom analyses using your own code. + +In order to apply an action on the imported data, select the **Add +Analysis** option on right clicking the the **Data Types** block. As +depicted in the figure below, you can see options to add a transform, +perform an analysis, add a destination sink or export the steps as +Jupyter notebook. You can also join and concatenate on the imported +dataset with other datasets. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-8.png + + + + +On selecting Add Analysis, the Analysis pane os shown, where you can +choose the analysis you want to perform. + +All analyses are generated using 100,000 rows of your dataset. + +You can add the following analysis to a dataframe: + +- Data visualizations, including histograms and scatter plots. +- A quick summary of your dataset, including number of entries, minimum + and maximum values (for numeric data), and most and least frequent + categories (for categorical data). +- A quick model of the dataset, which can be used to generate an + importance score for each feature. +- A target leakage report, which you can use to determine if one or + more features are strongly correlated with your target feature. +- A custom visualization using your own code. + +Following sections showcase few of the analysis techniques for the +Hotel-bookings data. + +Get Insights +------------ + +You can get the Data Insights report by selecting **Get Insights** +option for the **Data Types** block as shown in the figure. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/get-insights.png + + + + +| Please select the following parameters and hit **Create**. +| - ``Target column``: ``is-cancelled`` - ``Problem type``: + ``Classification`` + +After the report is generated, it outlines findings about statistics, +duplicate rows, warnings, confusion matrix and feature summary. This can +be a useful report before we start our detailed analysis. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/get-insights-report.png + + + +Table Summary +------------- + +Select **Table Summary** Analysis in the **Add Analysis** window. + +| Please select the following parameters and hit **Preview**. +| - ``Analysis name``: ``Table Summary`` + +Overall details of the data for various columsn is displayed as depicted +in figure below. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/table-information.png + + + +Select **Save** to save the analysis. + +Target Leakage +-------------- + +Target leakage occurs when there is data in a machine learning training +dataset that is strongly correlated with the target label, but is not +available in real-world data. For example, you may have a column in your +dataset that serves as a proxy for the column you want to predict with +your model. + +When you use the Target Leakage analysis, you specify the following: \* +**Target**: This is the feature about which you want your ML model to be +able to make predictions. \* **Problem type**: This is the ML problem +type on which you are working. Problem type can either be classification +or regression. \* (Optional) **Max features**: This is the maximum +number of features to present in the visualization, which shows features +ranked by their risk of being target leakage. + +For classification, the target leakage analysis uses the area under the +receiver operating characteristic, or AUC - ROC curve for each column, +up to Max features. For regression, it uses a coefficient of +determination, or R2 metric. + +The AUC - ROC curve provides a predictive metric, computed individually +for each column using cross-validation, on a sample of up to around 1000 +rows. A score of 1 indicates perfect predictive abilities, which often +indicates target leakage. A score of 0.5 or lower indicates that the +information on the column could not provide, on its own, any useful +information towards predicting the target. Although it can happen that a +column is uninformative on its own but is useful in predicting the +target when used in tandem with other features, a low score could +indicate the feature is redundant. + +| To create a target leakage analysis, Select **Target Leakage** + Analysis in the **Add Analysis** window. Please select the following + parameters and hit **Preview**. +| - ``Analysis name``: ``Target Leakage`` - ``Max features`` : ``40`` - + ``Target column``: ``is-cancelled`` - ``Problem type``: + ``Classification`` + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/target-leakage-pre.png + + + image + +For our example dataset, the image below shows a target leakage report +for a hotel booking cancellation problem, that is, predicting if a +person will cancel his hotel reservation or not. An AUC - ROC curve is +used to calculate the predictive ability of 31 raw features, out of +which ``reservation_status`` was determined to a target leakage. Also, +features - ``arrival_day_of_month``, ``babies``, +``reservation_status_date``, ``arrival_date_month``, +``reserved_room_type``, ``hotel`` and ``days_in_waiting_list`` were +identified as redundant. + +The identified features can be fairly omitted as part of the +transformations we will apply post this initial analysis. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/target-leakage.png + + +Select **Save** to save the analysis. + +Next, with SageMaker Data Wrangler’s feature correlation visualization +you can easily calculate the correlation of features in your data set +and visualize them as a correlation matrix. We will look into 2 types of +feature correlations and how to use them on our example dataset in hand. + +Feature Correlation (Linear) +---------------------------- + +Linear feature correlation is based on Pearson’s correlation. Numeric to +categorical correlation is calculated by encoding the categorical +features as the floating point numbers that best predict the numeric +feature before calculating Pearson’s correlation. Linear categorical to +categorical correlation is not supported. + +| Numeric to numeric correlation is in the range [-1, 1] where 0 implies + no correlation, 1 implies perfect correlation and -1 implies perfect + inverse correlation. Numeric to categorical and categorical to + categrical correlations are in the range [0, 1] where 0 implies no + correlation and 1 implies perfect correlation. To create the analysis, + choose **Feature Correlation** for the Analysis type and choose + **linear** for Correlation type. Please select the following + parameters and hit **Preview**. +| - ``Analysis name``: ``Linear Correlation`` + +This analysis will take a few minutes to complete. + +Features that are not either numeric or categorical are ignored. The +table below lists for each feature what is the most correlated feature +to it. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/linear-pre.png + +Based on the correlation values, we can see the top 6 feature pairs (as +listed below) are strongly correlating with one another. Also, some of +these features also showed up in the target analysis we did previously. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/linear-strongly-correlated.png + + +P.S.: A limit of 100,000 rows is used for this analysis. + +Select **Save** to save the analysis. + +Feature Correlation (Non-Linear) +-------------------------------- + +Non-linear feature correlation is based on Spearman’s rank correlation. +Numeric to categorical correlation is calculated by encoding the +categorical features as the floating point numbers that best predict the +numeric feature before calculating Spearman’s rank correlation. +Categorical to categorical correlation is based on the normalized +Cramer’s V test. + +Numeric to numeric correlation is in the range [-1, 1] where 0 implies +no correlation, 1 implies perfect correlation and -1 implies perfect +inverse correlation. Numeric to categorical and categorical to +categrical correlations are in the range [0, 1] where 0 implies no +correlation and 1 implies perfect correlation + +Features that are not either numeric or categorical are ignored. + +| To create the analysis, choose **Feature Correlation** for the + Analysis type and **non-linear** for Correlation type. Please select + the following parameters and hit **Preview**. +| - ``Analysis name``: ``Non-Linear Correlation`` + +This analysis will take a few minutes to complete. + +The table below lists for each feature what is the most correlated +feature to it. You can see most of the top correlated feature pairs +overlap with the previous two analyses. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/non-linear-pre.png + + +Select **Save** to save the analysis. + +Multicolinearity (Variance Inflation Factors) +--------------------------------------------- + +Variance Inflation Factor (VIF) is a measure of colinearity among +variables. It is calculated by solving a regression problem to predict +one variable given the rest. A VIF score is a positive number that is +greater or equal than 1, and a score of 1 means the variable is +completely independent of the others. The larger the score, the more +dependent it is. Since it is an inverse, it is possible for the VIF +score to be infinite. Note that we cap the VIF score at 50. As a rule of +thumb for cases where the number of samples is not abnormally small, a +score of up to 5 means the variable is only moderatly correlated, and +beyond 5 it is highly correlated. + +| To create the analysis for VIF, choose **Multicollinearity** for + Analysis type and choose **Variance inflation factors** for Analysis. + Please select the following parameters and hit **Preview**. +| - ``Analysis name``: ``Variance Inflation Factors`` + +This analysis will take a few minutes to complete. + +As per the above rule, we can eliminate the following feature columns +from our feature set since they will not contribute effectively towards +the prediction capability of the model that gets trained using these +features. + +- ``arrival_date_year`` +- ``adults``, ``agents`` +- ``arrival_date_week_number`` +- ``stays_in_week_nights`` + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/vif-pre.png + + +Select **Save** to save the analysis. + +Multicolinearity - Principal Component Analysis (PCA) +----------------------------------------------------- + +Principal Component Analysis (PCA) measures the variance of the data +along different directions in the feature space. The ordered list of +variances, also known as the singular values, can inform about +multicolinearity in our data. This list contains non-negative numbers. +When the numbers are roughly uniform, the data has very few +multicolinearities. However, when the opposite is true, the magnitude of +the top values will dominate the rest. In order to avoid issues related +to different scales, the individual features are standardized to have +mean 0 and standard deviation 1 before applying PCA. + +| To create the analysis for PCA, choose **Multicollinearity** for + Analysis type and choose **Principal component analysis** for + Analysis. Please select the following parameters and hit **Preview**. +| - ``Analysis name``: ``Principal Component Analysis`` + +This analysis will take a few minutes to complete. + +As per the above rule, it is evident the numbers (variances) are not +uniform hence confirming that the data has multicolinearies to fix. This +has already been confirmed by our previous analysis. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/pca-pre.png + +Select **Save** to save the analysis. + +Multicolinearity Lasso Feature Selection +---------------------------------------- + +Lasso feature selection trains a linear classifier with L1 +regularization (you can control the strength of L1 penalty by adjusting +“L1 magnitude”) that induces a sparse solution. The regressor provides a +coefficient for each feature, and the absolute value of this coefficient +could be interpreted as an importance score for that feature. + +| To create the analysis for Lasso Feature Selection, choose + **Multicollinearity** for Analysis type and choose **Lasso feature + selection** for Analysis. Please select the following parameters and + hit **Preview**. +| - ``Analysis name``: ``Non-Linear Correlation`` - ``L1 Magnitude``: + ``1`` - ``Problem Type``: ``Classification`` - ``Label Column`` : + ``is_cancelled`` + +This analysis will take a few minutes to complete. + +The plot below provides features’ importance scores (absolute +coefficients) after training a classifier on a sample of the dataset +(10k for large dataset). The training process includes a standardization +of the features to have mean 0 and standard deviation 1 in order to +avoid a skewed importance score due to different scales. + +The classifier obtained a roc_auc score: ``0.639269142214666``. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/lasso-pre.png + + +Select **Save** to save the analysis. + +Detect Duplicate Rows +--------------------- + +Next, with the new duplicate row detection visualization, you can +quickly detect if your data set has any duplicate rows. To apply this +analysis, choose **Duplicate rows** for Analysis type. + +From the figure bwlow, we can see almost ~33% of the rows in the dataset +are duplicates. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/duplicate-rows.png + + +Quick Model +----------- + +We can create a quick model using the raw features to determine how good +our features are, prior to applying transformations. + +Use the Quick Model visualization to quickly evaluate your data and +produce importance scores for each feature. A feature importance score +indicates how useful a feature is at predicting a target label. The +feature importance score is between [0, 1] and a higher number indicates +that the feature is more important to the whole dataset. On the top of +the quick model chart, there is a model score. A classification problem +shows an F1 score. A regression problem has a mean squared error (MSE) +score. + +When you create a quick model chart, you select a dataset you want +evaluated, and a target label against which you want feature importance +to be compared. Data Wrangler does the following: \* Infers the data +types for the target label and each feature in the dataset selected. \* +Determines the problem type. Based on the number of distinct values in +the label column, Data Wrangler determines if this is a regression or +classification problem type. Data Wrangler sets a categorical threshold +to 100. If there are more than 100 distinct values in the label column, +Data Wrangler classifies it as a regression problem; otherwise, it is +classified as a classification problem. \* Pre-processes features and +label data for training. The algorithm used requires encoding features +to vector type and encoding labels to double type. \* Trains a random +forest algorithm with 70% of data. Spark’s RandomForestRegressor is used +to train a model for regression problems. The RandomForestClassifier is +used to train a model for classification problems. \* Evaluates a random +forest model with the remaining 30% of data. Data Wrangler evaluates +classification models using an F1 score and evaluates regression models +using an MSE (mean squared error) score. \* Calculates feature +importance for each feature using the Gini importance method. + +Let us create a prediction model on the fly for the problem for the +booking cancellation problem using the raw crude features we started +with in Data Wrangler’s Quick Model option. + +| Please choose **Quick Model** for Analysis type. Select the following + parameters and hit **Preview**. +| - ``Analysis name``: ``Model pre-transform`` + +This analysis will take a few minutes to complete. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/create-quick-model.png + + + + +A limit of 100,000 rows is used for this analysis. You can use the Quick +Model feature to provide a rough estimate of the expected predicted +quality and the predictive power of the features in your dataset. + +We can from the results below, Quick model was able to predict with an +F1 score of 82% on the test set. But, this is misleading, given we +haven’t eliminated most of the feature columns that are a target leakage +or redundant based on high colinearity. This is justified in the results +below where the column ``reservation_status`` which is a target leakage +ranked as the most important feature. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/quick-model-pre.png + + + + +Select **Save** to save the model. + +Next Steps +---------- +.. toctree:: + :maxdepth: 1 + + ./data-transdormations/index diff --git a/sagemaker-datawrangler/tabular-dataflow/data-import/Data-Import.md b/sagemaker-datawrangler/tabular-dataflow/data-import/Data-Import.md new file mode 100644 index 0000000000..a71a73153d --- /dev/null +++ b/sagemaker-datawrangler/tabular-dataflow/data-import/Data-Import.md @@ -0,0 +1,49 @@ +# Importing Dataset into Data Wrangler using SageMaker Studio + +Following steps outline how to import data into Sagemaker to be consumed by Data Wrangler + + +Steps to import data

+1. Initialize SageMaker Data Wrangler via SageMaker Studio UI. You can use any one of the options specified below. + + + + - Option 1.
Use the Sage Maker Launcher screen as depicted here: + + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-1.png) + + - Option 2.
You can use the SageMaker resources menu on the left, selecting Data Wrangler, and new flow + + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-1-1.png) + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-1-2.png) + - Option 3.
You can also use the File -> New -> DataWrangler option as shown here + + ![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-1-3.png) +2. Data Wrangler takes a few minutes to load. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-2.png) +3. Once Data Wrangler is loaded, you should be able to see it under running instances and apps as shown below. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-3.png) + +4. Once Data Wrangler is up and running, you can see the following data flow interface with options for import, creating data flows and export as shown below. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-4.png) + +5. Make sure to rename the untitled.flow to your preference (for e.g., hotel-bookings.flow) + +6. Now you will have the option to select your data source. Because the data is in Amazon S3, select Amazon S3 to import data. Paste the S3 URL for the hotel-bookings.csv file into the search box below and hit go. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-5.png) + +7. Data Wrangler will show you a preview of the data. Select the CSV file from the drop down results. On the right pane, make sure COMMA is chosen as the delimiter and Sampling is *None*. Our data set is small enough to run Data Wrangler transformations on the full data set. If you have a large data set, consider using sampling. Finally select *Import* to import this dataset to Data Wrangler. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-6.png) + +8. Once the dataset is imported, Data Wrangler automatically validates the dataset and detects the data types. The flow editor now shows 2 blocks showcasing that the data was imported from a source and data types recognized. You are also allowed to edit the data types if needed. + +![image](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-7.png) + +### Next Steps + +As a next step, we will explore the data that we uploaded. Please refer to **[Exploratory Data Analysis](./Data-Exploration.md)** and follow steps for Data exploration. diff --git a/sagemaker-datawrangler/tabular-dataflow/data-import/index.rst b/sagemaker-datawrangler/tabular-dataflow/data-import/index.rst new file mode 100644 index 0000000000..76ce7141f4 --- /dev/null +++ b/sagemaker-datawrangler/tabular-dataflow/data-import/index.rst @@ -0,0 +1,87 @@ +Importing Dataset into Data Wrangler using SageMaker Studio +=========================================================== + +Following steps outline how to import data into Sagemaker to be consumed +by Data Wrangler + +| Steps to import data +| 1. Initialize SageMaker Data Wrangler via SageMaker Studio UI. You can + use any one of the options specified below. + + + +Option 1 : Use the Sage Maker Launcher screen +-------------------------------------------- + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-1.png + + +Option 2 : You can use the SageMaker resources menu on the left, selecting Data Wrangler, and new flow +---------------------------------------------------------------------------- + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-1-1.png + + + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-1-2.png + + +Option 3. : You can also use the File -> New -> DataWrangler option as shown here +-------------------------------------------------------------------------------- +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-1-3.png + + +2. Data Wrangler takes a few minutes to load. + +|image| + +3. Once Data Wrangler is loaded, you should be able to see it +under running instances and apps as shown below. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-3.png + + +4. Once Data Wrangler is up and running, you can see the following data + flow interface with options for import, creating data flows and + export as shown below. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-4.png + + +5. Make sure to rename the untitled.flow to your preference (for e.g., + hotel-bookings.flow) + +6. Now you will have the option to select your data source. Because the + data is in Amazon S3, select Amazon S3 to import data. Paste the S3 + URL for the hotel-bookings.csv file into the search box below and hit + go. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-5.png + + +7. Data Wrangler will show you a preview of the data. Select the CSV + file from the drop down results. On the right pane, make sure COMMA + is chosen as the delimiter and Sampling is *None*. Our data set is + small enough to run Data Wrangler transformations on the full data + set. If you have a large data set, consider using sampling. Finally + select *Import* to import this dataset to Data Wrangler. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-6.png + + +8. Once the dataset is imported, Data Wrangler automatically validates + the dataset and detects the data types. The flow editor now shows 2 + blocks showcasing that the data was imported from a source and data + types recognized. You are also allowed to edit the data types if + needed. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/image-7.png + + +Next Steps +---------- +.. toctree:: + :maxdepth: 1 + + ./data-exploration/index + diff --git a/sagemaker-datawrangler/tabular-dataflow/data-transformations/Data-Transformations.md b/sagemaker-datawrangler/tabular-dataflow/data-transformations/Data-Transformations.md new file mode 100644 index 0000000000..d2a782f538 --- /dev/null +++ b/sagemaker-datawrangler/tabular-dataflow/data-transformations/Data-Transformations.md @@ -0,0 +1,195 @@ +# Data Transformations + + +Based on the Data explorations carried out in previous step, we are now ready to apply transformations to the data. +Amazon SageMaker Data Wrangler provides numerous ML data transforms to streamline cleaning, transforming, and featurizing your data. When you add a transform, it adds a step to the data flow. Each transform you add modifies your dataset and produces a new dataframe. All subsequent transforms apply to the resulting dataframe. + +Data Wrangler includes built-in transforms, which you can use to transform columns without any code. You can also add custom transformations using PySpark, Pandas, and PySpark SQL. Some transforms operate in place, while others create a new output column in your dataset. + +In order to apply an action on the imported data, select the **Add Transform** option on right clicking the the **Data Types** block. In the displayed window, select **Add Steps** to display **Add Transform** window. + + ![add-transform](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/add-transform.png) + + + +### Drop Columns + Now we will drop columns based on the analyses we performed in the previous section. + +- based on target leakage : drop `reservation_status` + +- redundant columns : drop columns that are redundant - `days_in_waiting_list`, `hotel`, `reserved_room_type`, `arrival_date_month`, `reservation_status_date`, `babies` and `arrival_date_day_of_month` + +- based on linear correlation results : drop columns `arrival_date_week_number`, `arrival_date_year` as correlation values for these feature (column) pairs are greater than the recommended threshold of 0.90. + +- based on non-linear correlation results: drop `reservation_status`. This column was already marked to be dropped based on Target leakage analysis. + + we can drop all these columns in one go. To drop columns, choose **Manage columns** transform from the **Add Transform** window. Then select the **Drop column** option from **Manage columns** transform. + +Please select the the column names we want to drop as shown in the image below and hit **Preview**. + + + ![drop-columns](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/drop-columns.png) + +If the Preview is OK, click **Add** to add the transform in the data flow. + +Further, based on the multi-colinearity analysis results, we can also drop the columns `adults` and `agent` for whom the variance inflation factor scores are greater than 5. Please select the the column names we want to drop and hit **Preview**. + + + + ![drop-more-columns](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/drop-more-cols.png) + + If the Preview is OK, click **Add** to add the transform in the data flow. + + +### Drop Duplicate Rows +To drop the duplicate rows that we identified based on the analysis we did in the previous section. To drop columns, choose **Manage rows** transform from the **Add Transform** window. Then select the **Drop duplicates** option from **Manage rows** transform and hit **Preview**. + + + ![drop-duplicates](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/drop-duplicates.png) + + If the Preview is OK, click **Add** to add the transform in the data flow. + + +### Handle Outliers +An outlier can cause serious problems in statistical analysis. Machine learning models are sensitive to the distribution and range of feature values. Outliers, or rare values, can negatively impact model accuracy and lead to longer training times. When you define a Handle outliers transform step, the statistics used to detect outliers are generated on the data available in Data Wrangler when defining this step. These same statistics are used when running a Data Wrangler job. + +To handle outliers, choose **Handle outliers** transform from the **Add Transform** window. +Please select the following parameters and hit **Preview**. + +- `Transform`: `Standard deviation numeric outliers` +- `Fix method` : `Remove` +- `Standard deviations`: `4` +- `Input columns`: `lead_time`,`stays_in_weekend_nights`, `stays_in_weekday_nights`, `is_repeated_guest`, `prev_cancellations`, `prev_bookings_not_canceled`, `booking_changes`, `adr`, `total_of_specical_requests`, `required_car_parking_spaces`, + + + +![outliers](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/handle-outliers.png) + + + If the Preview is OK, click **Add** to add the transform in the data flow. + + +### Handle Missing Values +To handle outliers, choose **Handle missing values** transform from the **Add Transform** window. We can do the following to handle missing values in our feature columns using Data Wrangler. + + + - Missing values in **Children** column : Majority of the visitors were not accompanied by children and hence missing data can be replaced by number of children = 0. + +![fill-missing-children](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/fill-missing-children-val.png) + + Please hit **Preview** to look at the transform preview, and if it is OK, click **Add** to add the transform in the data flow. + +- Missing values in **Country** column +Iterating through the country column reveals that most of the clients are from Europe. Therefore, all the missing values in the country column are replaced with the country of maximum occurrence - Portugal (PRT). Fill missing country column with `PRT` based on value counts + +![fill-missing-country](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/fill-missing-country-val.png) + +Please hit **Preview** to look at the transform preview, and if it is OK, click **Add** to add the transform in the data flow. + +- Custom Transform - Meal type has Undefined category, changing the Undefined value to the most used which is BB by implementing a custom pyspark transform with two simple lines of code. This can be done by choosing **Custom transform** transform from the **Add Transform** window. Specify the name of the transform, and paste following code in the transform as shown in figure below. + + ![custom-pyspark](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/custom-pyspark-code.png) + + +```python +from pyspark.sql.functions import when + +df = df.withColumn('meal', when(df.meal == 'Undefined', 'BB').otherwise(df.meal)) +``` + +Please hit **Preview** to look at the transform preview, and if it is OK, click **Add** to add the transform in the data flow. + + ### Numeric Normalization +Normalization is a scaling technique in which values are shifted and rescaled so that they end up ranging between 0 and 1. It is also known as Min-Max scaling. Standardization is another scaling technique where the values are centered around the mean with a unit standard deviation. This means that the mean of the attribute becomes zero and the resultant distribution has a unit standard deviation. + +For our example use case, let's normalize the numeric feature columns to a standard scale [0,1]. + +From Data Wrangler's list of pre-built transforms, choose **Process numeric**. Please select the following parameters and hit **Preview**. + +- `Transform`: `Scale values` +- `Scalar` : `Min-max scalar` +- `Min`: `0` +- `Max`: `1` +- `Input columns`: `lead_time`,`stays_in_weekend_nights`, `stays_in_weekday_nights`, `is_repeated_guest`, `prev_cancellations`, `prev_bookings_not_canceled`, `booking_changes`, `adr`, `total_of_specical_requests`, `required_car_parking_spaces` + + + ![scale-numeric](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/min-max.png) + + + If the Preview is OK, click **Add** to add the transform in the data flow. + +### Handle Categorical Data + +Categorical data is usually composed of a finite number of categories, where each category is represented with a string. Encoding categorical data is the process of creating a numerical representation for categories. With Data Wrangler, we can select Ordinal encode to encode categories into an integer between 0 and the total number of categories in the Input column you select. Select one-hot encoding or use similarity encoding when you have a large number of categorical variables and Noisy data. + + +From Data Wrangler's list of pre-built transforms, choose **Encode Categorical**. Please select the following parameters and hit **Preview**. +- `Transform`: `One-hot encode` +- `Invalid handling strategy` : `Keep` +- `Output style`: `Columns` +- `Max`: `1` +- `Input columns`: `meal`, `is_repeated_guest`, `market_segment`, `assigned_room_type`, `deposit_type`, `customer_type` + + + + ![scale-categorical](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/categorical-one-hot.png) + + + If the Preview is OK, click **Add** to add the transform in the data flow. + + +### Balancing the target variable + +DataWrangler also helps to balance the target variable (column) for class imbalance. Let's presume the following for the negative and positive cases. + + is_canceled = 0 (negative case) + is_canceled = 1 (positive case) + +In Data Wrangler, we can handle class imbalance using 3 different techniques. + + - Random Undersample + - Random Oversample + - SMOTE + +From the Data Wrangler's transform pane, choose **Balance Data** as the transform. Please select the following parameters as shown in image below and hit **Preview**. +- `Target column`: `is_canceled` +- `Desiered ratio` : `1` +- `Transform`: `Random oversample` + + ![random-oversample](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/random-oversample.png) + + + If the Preview is OK, click **Add** to add the transform in the data flow. + +The state of the classes before and after balancing is as follows: + +The ratio of positive to negative case = ~0.38 + +![pre-balance](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/class-before-balance.png) + + +After balancing, the ratio is 1 +![post-balance](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/class-after-balance.png) + +### Quick Model +Given, we have applied most of the needed transformations on our feature columns, we can now create a Quick Model again using the transformed features to identify the predictive ability of our features and take a look at their attribution towards prediction. + +It is a good practice to run a Quick Model everytime we make a set of feature transforms. Previously, we ran a Quick Model analysis using the raw features. The results of this previous run was mostly incorrect and misleading, given, we haven't fixed most of the correlation and other issues with our dataset. + +The below figure shows the results of the newly run Quick Model created using the transformed features. As you can see, the Quick Model achieved an F1 score of 62% on the test data. The top 5 most contributing features towards this score are as follows which is different from what we see previously. + + lead_time + country + customer_type_Transient + required_car_parking_spaces + booking_changes + +Craete a quick model, similar to one we created in the **[Exploratory Data Analysis](./Data-Exploration.md)** step. + +![post-quick-model](https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/quick-model-post.png) + +Compare the model with the one created in Exploratory Data Analysis step. + +### Next Steps + +As a next step, we will export the transformed data for further use. Please refer to **[Readme](./README.md)** and follow steps for Data Export. diff --git a/sagemaker-datawrangler/tabular-dataflow/data-transformations/index.rst b/sagemaker-datawrangler/tabular-dataflow/data-transformations/index.rst new file mode 100644 index 0000000000..942304a959 --- /dev/null +++ b/sagemaker-datawrangler/tabular-dataflow/data-transformations/index.rst @@ -0,0 +1,301 @@ +Data Transformations +==================== + +Based on the Data explorations carried out in previous step, we are now +ready to apply transformations to the data. Amazon SageMaker Data +Wrangler provides numerous ML data transforms to streamline cleaning, +transforming, and featurizing your data. When you add a transform, it +adds a step to the data flow. Each transform you add modifies your +dataset and produces a new dataframe. All subsequent transforms apply to +the resulting dataframe. + +Data Wrangler includes built-in transforms, which you can use to +transform columns without any code. You can also add custom +transformations using PySpark, Pandas, and PySpark SQL. Some transforms +operate in place, while others create a new output column in your +dataset. + +In order to apply an action on the imported data, select the **Add +Transform** option on right clicking the the **Data Types** block. In +the displayed window, select **Add Steps** to display **Add Transform** +window. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/add-transform.png + + +Drop Columns +------------ + +Now we will drop columns based on the analyses we performed in the +previous section. + +- based on target leakage : drop ``reservation_status`` + +- redundant columns : drop columns that are redundant - + ``days_in_waiting_list``, ``hotel``, ``reserved_room_type``, + ``arrival_date_month``, ``reservation_status_date``, ``babies`` and + ``arrival_date_day_of_month`` + +- based on linear correlation results : drop columns + ``arrival_date_week_number``, ``arrival_date_year`` as correlation + values for these feature (column) pairs are greater than the + recommended threshold of 0.90. + +- based on non-linear correlation results: drop ``reservation_status``. + This column was already marked to be dropped based on Target leakage + analysis. + +we can drop all these columns in one go. To drop columns, choose +**Manage columns** transform from the **Add Transform** window. Then +select the **Drop column** option from **Manage columns** transform. + +Please select the the column names we want to drop as shown in the image +below and hit **Preview**. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/drop-columns.png + +If the Preview is OK, click **Add** to add the transform in the data +flow. + +Further, based on the multi-colinearity analysis results, we can also +drop the columns ``adults`` and ``agent`` for whom the variance +inflation factor scores are greater than 5. Please select the the column +names we want to drop and hit **Preview**. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/drop-more-cols.png + +If the Preview is OK, click **Add** to add the transform in the data +flow. + +Drop Duplicate Rows +------------------- + +We should drop the duplicate rows that we identified based on the analysis we +did in the previous section. To drop columns, choose **Manage rows** +transform from the **Add Transform** window. Then select the **Drop +duplicates** option from **Manage rows** transform and hit **Preview**. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/drop-duplicates.png + +If the Preview is OK, click **Add** to add the transform in the data +flow. + +Handle Outliers +--------------- + +An outlier can cause serious problems in statistical analysis. Machine +learning models are sensitive to the distribution and range of feature +values. Outliers, or rare values, can negatively impact model accuracy +and lead to longer training times. When you define a Handle outliers +transform step, the statistics used to detect outliers are generated on +the data available in Data Wrangler when defining this step. These same +statistics are used when running a Data Wrangler job. + +| To handle outliers, choose **Handle outliers** transform from the + **Add Transform** window. +| Please select the following parameters and hit **Preview**. + +- ``Transform``: ``Standard deviation numeric outliers`` +- ``Fix method`` : ``Remove`` +- ``Standard deviations``: ``4`` +- ``Input columns``: ``lead_time``,\ ``stays_in_weekend_nights``, + ``stays_in_weekday_nights``, ``is_repeated_guest``, + ``prev_cancellations``, ``prev_bookings_not_canceled``, + ``booking_changes``, ``adr``, ``total_of_specical_requests``, + ``required_car_parking_spaces``, + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/handle-outliers.png + + +If the Preview is OK, click **Add** to add the transform in the data +flow. + +Handle Missing Values +--------------------- + +To handle outliers, choose **Handle missing values** transform from the +**Add Transform** window. We can do the following to handle missing +values in our feature columns using Data Wrangler. + +- Missing values in **Children** column : Majority of the visitors were + not accompanied by children and hence missing data can be replaced by + number of children = 0. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/fill-missing-children-val.png + + +Please hit **Preview** to look at the transform preview, and if it is +OK, click **Add** to add the transform in the data flow. + +- Missing values in **Country** column Iterating through the country + column reveals that most of the clients are from Europe. Therefore, + all the missing values in the country column are replaced with the + country of maximum occurrence - Portugal (PRT). Fill missing country + column with ``PRT`` based on value counts + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/fill-missing-country-val.png + + +Please hit **Preview** to look at the transform preview, and if it is +OK, click **Add** to add the transform in the data flow. + +- Custom Transform - Meal type has Undefined category, changing the + Undefined value to the most used which is BB by implementing a custom + pyspark transform with two simple lines of code. This can be done by + choosing **Custom transform** transform from the **Add Transform** + window. Specify the name of the transform, and paste following code + in the transform as shown in figure below. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/custom-pyspark-code.png + +.. code:: python + + from pyspark.sql.functions import when + + df = df.withColumn('meal', when(df.meal == 'Undefined', 'BB').otherwise(df.meal)) + +Please hit **Preview** to look at the transform preview, and if it is +OK, click **Add** to add the transform in the data flow. + +### Numeric Normalization Normalization is a scaling technique in which +values are shifted and rescaled so that they end up ranging between 0 +and 1. It is also known as Min-Max scaling. Standardization is another +scaling technique where the values are centered around the mean with a +unit standard deviation. This means that the mean of the attribute +becomes zero and the resultant distribution has a unit standard +deviation. + +For our example use case, let’s normalize the numeric feature columns to +a standard scale [0,1]. + +From Data Wrangler’s list of pre-built transforms, choose **Process +numeric**. Please select the following parameters and hit **Preview**. + +- ``Transform``: ``Scale values`` +- ``Scalar`` : ``Min-max scalar`` +- ``Min``: ``0`` +- ``Max``: ``1`` +- ``Input columns``: ``lead_time``,\ ``stays_in_weekend_nights``, + ``stays_in_weekday_nights``, ``is_repeated_guest``, + ``prev_cancellations``, ``prev_bookings_not_canceled``, + ``booking_changes``, ``adr``, ``total_of_specical_requests``, + ``required_car_parking_spaces`` + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/min-max.png + +If the Preview is OK, click **Add** to add the transform in the data +flow. + +Handle Categorical Data +----------------------- + +Categorical data is usually composed of a finite number of categories, +where each category is represented with a string. Encoding categorical +data is the process of creating a numerical representation for +categories. With Data Wrangler, we can select Ordinal encode to encode +categories into an integer between 0 and the total number of categories +in the Input column you select. Select one-hot encoding or use +similarity encoding when you have a large number of categorical +variables and Noisy data. + +| From Data Wrangler’s list of pre-built transforms, choose **Encode + Categorical**. Please select the following parameters and hit + **Preview**. +| - ``Transform``: ``One-hot encode`` - ``Invalid handling strategy`` : + ``Keep`` - ``Output style``: ``Columns`` - ``Max``: ``1`` +| - ``Input columns``: ``meal``, ``is_repeated_guest``, + ``market_segment``, ``assigned_room_type``, ``deposit_type``, + ``customer_type`` + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/categorical-one-hot.png + + +If the Preview is OK, click **Add** to add the transform in the data +flow. + +Balancing the target variable +----------------------------- + +DataWrangler also helps to balance the target variable (column) for +class imbalance. Let’s presume the following for the negative and +positive cases. + +:: + + is_canceled = 0 (negative case) + is_canceled = 1 (positive case) + +In Data Wrangler, we can handle class imbalance using 3 different +techniques. + +:: + + - Random Undersample + - Random Oversample + - SMOTE + +| From the Data Wrangler’s transform pane, choose **Balance Data** as + the transform. Please select the following parameters as shown in + image below and hit **Preview**. +| - ``Target column``: ``is_canceled`` - ``Desiered ratio`` : ``1`` - + ``Transform``: ``Random oversample`` + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/random-oversample.png + + +If the Preview is OK, click **Add** to add the transform in the data +flow. + +The state of the classes before and after balancing is as follows: + +The ratio of positive to negative case = ~0.38 + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/class-before-balance.png + + +After balancing, the ratio is 1 |post-balance| + +Quick Model +----------- + +Given, we have applied most of the needed transformations on our feature +columns, we can now create a Quick Model again using the transformed +features to identify the predictive ability of our features and take a +look at their attribution towards prediction. + +It is a good practice to run a Quick Model everytime we make a set of +feature transforms. Previously, we ran a Quick Model analysis using the +raw features. The results of this previous run was mostly incorrect and +misleading, given, we haven’t fixed most of the correlation and other +issues with our dataset. + +The below figure shows the results of the newly run Quick Model created +using the transformed features. As you can see, the Quick Model achieved +an F1 score of 62% on the test data. The top 5 most contributing +features towards this score are as follows which is different from what +we see previously. + +:: + + lead_time + country + customer_type_Transient + required_car_parking_spaces + booking_changes + +Craete a quick model, similar to one we created in the `Exploratory Data +Analysis <./Data-Exploration.md>`__ step. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/quick-model-post.png + +Compare the model with the one created in Exploratory Data Analysis +step. + +Next Steps +---------- + +As a next step, we will export the transformed data for further use. +Please follow steps for Data +Export. + +.. |post-balance| image:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/class-after-balance.png diff --git a/sagemaker-datawrangler/tabular-dataflow/index.rst b/sagemaker-datawrangler/tabular-dataflow/index.rst new file mode 100644 index 0000000000..8fc5c1a3d3 --- /dev/null +++ b/sagemaker-datawrangler/tabular-dataflow/index.rst @@ -0,0 +1,361 @@ + +Tabular Dataflow Example +======================== +This example provide quick walkthrough of how to aggregate and prepare +data for Machine Learning using Amazon SageMaker Data Wrangler for +Tabular dataset. + +Hotel Booking Demand Example +---------------------------- + +Background +---------- + +Amazon SageMaker helps data scientists and developers to prepare, build, +train, and deploy machine learning models quickly by bringing together a +broad set of purpose-built capabilities. This example shows how +SageMaker can accelerate machine learning development during the data +preprocessing stage to help process the hotel demand data and find +relevant features for the training model to predict hotel cancellations. + +Dataset +~~~~~~~ + +.. raw:: html + +
+ +Dataset + +We are using the `Hotel Booking Demand +dataset `__ +that is publically available. This data set contains booking information +for a city hotel and a resort hotel, and includes information such as +when the booking was made, length of stay, the number of adults, +children, and/or babies, and the number of available parking spaces, +among other things. + +The data needs to be downloaded from the locations specified, and +uploaded to S3 bucket before we start the Data Preprocessing phase. +Please follow the Experiment Steps outlined in later sections, to +download the data and notebooks. + +Description of the Columns +-------------------------- + ++-----------------------------------+-----------------------------------+ +| Column Name | Description | ++===================================+===================================+ +| ``hotel`` | Type of the hotel (``H1`` = | +| | Resort Hotel or ``H2`` = City | +| | Hotel) | ++-----------------------------------+-----------------------------------+ +| ``is_canceled`` | Value indicating if the booking | +| | was canceled (1) or not (0) | ++-----------------------------------+-----------------------------------+ +| ``lead_time`` | Number of days that elapsed | +| | between the entering date of the | +| | booking into the PMS and the | +| | arrival date | ++-----------------------------------+-----------------------------------+ +| ``arrival_date_year`` | Year of arrival date | ++-----------------------------------+-----------------------------------+ +| ``arrival_date_month`` | Month of arrival date | ++-----------------------------------+-----------------------------------+ +| ``arrival_date_week_number`` | Week number of year for arrival | +| | date | ++-----------------------------------+-----------------------------------+ +| ``arrival_date_day_of_month`` | Day of arrival date | ++-----------------------------------+-----------------------------------+ +| ``stays_in_weekend_nights`` | Number of weekend nights | +| | (Saturday or Sunday) the guest | +| | stayed or booked to stay at the | +| | hotel | ++-----------------------------------+-----------------------------------+ +| ``stays_in_week_nights`` | Number of week nights (Monday to | +| | Friday) the guest stayed or | +| | booked to stay at the hotel | ++-----------------------------------+-----------------------------------+ +| ``adults`` | Number of adults | ++-----------------------------------+-----------------------------------+ +| ``children`` | Number of children | ++-----------------------------------+-----------------------------------+ +| ``babies`` | Number of babies | ++-----------------------------------+-----------------------------------+ +| ``meal`` | Type of meal booked. Categories | +| | are presented in standard | +| | hospitality meal packages: | +| | ``Undefined/SC`` – no meal | +| | package; ``BB`` – Bed & | +| | Breakfast; ``HB`` – Half board | +| | (breakfast and one other meal – | +| | usually dinner); ``FB`` – Full | +| | board (breakfast, lunch and | +| | dinner) | ++-----------------------------------+-----------------------------------+ +| ``country`` | Country of origin. Categories are | +| | represented in the | +| | ``ISO 3155–3:2013`` format | ++-----------------------------------+-----------------------------------+ +| ``market_segment`` | Market segment designation. In | +| | categories, the term ``TA`` means | +| | “Travel Agents” and ``TO`` means | +| | “Tour Operators” | ++-----------------------------------+-----------------------------------+ +| ``distribution_channel`` | Booking distribution channel. The | +| | term ``TA`` means “Travel Agents” | +| | and ``TO`` means “Tour Operators” | ++-----------------------------------+-----------------------------------+ +| ``is_repeated_guest`` | Value indicating if the booking | +| | name was from a repeated guest | +| | (1) or not (0) | ++-----------------------------------+-----------------------------------+ +| ``previous_cancellations`` | Number of previous bookings that | +| | were cancelled by the customer | +| | prior to the current booking | ++-----------------------------------+-----------------------------------+ +| ` | Number of previous bookings not | +| `previous_bookings_not_canceled`` | cancelled by the customer prior | +| | to the current booking | ++-----------------------------------+-----------------------------------+ +| ``reserved_room_type`` | Code of room type reserved. Code | +| | is presented instead of | +| | designation for anonymity | +| | reasons. | ++-----------------------------------+-----------------------------------+ +| ``assigned_room_type`` | Code for the type of room | +| | assigned to the booking. | +| | Sometimes the assigned room type | +| | differs from the reserved room | +| | type due to hotel operation | +| | reasons (e.g. overbooking) or by | +| | customer request. Code is | +| | presented instead of designation | +| | for anonymity reasons. | ++-----------------------------------+-----------------------------------+ +| ``booking_changes`` | Number of changes/amendments made | +| | to the booking from the moment | +| | the booking was entered on the | +| | PMS until the moment of check-in | +| | or cancellation | ++-----------------------------------+-----------------------------------+ +| ``deposit_type`` | Indication on if the customer | +| | made a deposit to guarantee the | +| | booking. This variable can assume | +| | three categories: No Deposit – no | +| | deposit was made; ``Non Refund`` | +| | – a deposit was made in the value | +| | of the total stay cost; | +| | ``Refundable`` – a deposit was | +| | made with a value under the total | +| | cost of stay. | ++-----------------------------------+-----------------------------------+ +| ``agent`` | ID of the travel agency that made | +| | the booking | ++-----------------------------------+-----------------------------------+ +| ``company`` | ID of the company/entity that | +| | made the booking or responsible | +| | for paying the booking. ID is | +| | presented instead of designation | +| | for anonymity reasons | ++-----------------------------------+-----------------------------------+ +| ``days_in_waiting_list`` | Number of days the booking was in | +| | the waiting list before it was | +| | confirmed to the customer | ++-----------------------------------+-----------------------------------+ +| ``customer_type`` | Type of booking, assuming one of | +| | four categories: ``Contract`` - | +| | when the booking has an allotment | +| | or other type of contract | +| | associated to it; ``Group`` – | +| | when the booking is associated to | +| | a group; ``Transient`` – when the | +| | booking is not part of a group or | +| | contract, and is not associated | +| | to other transient booking; | +| | ``Transient-party`` – when the | +| | booking is transient, but is | +| | associated to at least other | +| | transient booking | ++-----------------------------------+-----------------------------------+ +| ``adr`` | Average Daily Rate as defined by | +| | dividing the sum of all lodging | +| | transactions by the total number | +| | of staying nights | ++-----------------------------------+-----------------------------------+ +| ``required_car_parking_spaces`` | Number of car parking spaces | +| | required by the customer | ++-----------------------------------+-----------------------------------+ +| ``total_of_special_requests`` | Number of special requests made | +| | by the customer (e.g. twin bed or | +| | high floor) | ++-----------------------------------+-----------------------------------+ +| ``reservation_status`` | Reservation last status, assuming | +| | one of three categories: | +| | ``Canceled`` – booking was | +| | canceled by the customer; | +| | ``Check-Out`` – customer has | +| | checked in but already departed; | +| | ``No-Show`` – customer did not | +| | check-in and did inform the hotel | +| | of the reason why | ++-----------------------------------+-----------------------------------+ +| ``reservation_status_date`` | Date at which the last status was | +| | set. This variable can be used in | +| | conjunction with the | +| | ReservationStatus to understand | +| | when was the booking canceled or | +| | when did the customer checked-out | +| | of the hotel | ++-----------------------------------+-----------------------------------+ + +-------------- + +Pre-requisites: +--------------- + +- We need to ensure dataset (tracks and ratings dataset) for ML is + uploaded to a data source (instructions to download the dataset to + Amazon S3 is available in the following section). +- Data source can be any one of the following options: + + - S3 + - Athena + - RedShift + - SnowFlake + +.. container:: alert alert-block alert-info + + Data Source + + For this experiment the Data Source will be `Amazon + S3 `__ + +Experiment steps +---------------- + +Downloading the dataset +~~~~~~~~~~~~~~~~~~~~~~~ + +- Ensure that you have a working `Amazon SageMaker + Studio `__ environment and + that it has been updated. + +- Follow the steps below to download the dataset. + +1. Download the `Hotel Booking Demand + dataset `__ + from the specified location. +2. Create a private S3 bucket to upload the dataset in. You can + reference the instructions for bucket creaiton [here] + https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html +3. Upload the data in step 1 to the bucket created in step 2. Steps to + upload the data can be found [here] + https://docs.aws.amazon.com/AmazonS3/latest/userguide/upload-objects.html +4. Note the S3 URL for the file uploaded in Step 3 before moving to the + next sextion. This data will be used as input to the Datawrangler. + The S3 URL will be used in the next step. + +Data Import from S3 to Data Wrangler +------------------------------------ + + + +.. toctree:: + :maxdepth: 1 + :caption: Data Import from S3 to Data Wrangler + + data-import/index + + +Exploratory Data Analysis +-------------------------- + + +.. toctree:: + :maxdepth: 1 + :caption: Data Exploration from S3 to Data Wrangler + + data-exploration/index + +Data Transformation +------------------- + + +.. toctree:: + :maxdepth: 1 + :caption: Data transformations from S3 to Data Wrangler + + data-transformations/index + +Data Export +~~~~~~~~~~~ + +Data Wrangler UI can also be used to export the transformed data to +Amazon S3. To get started with this process, first let’s create a +destination node. Right click on the final transform on your data and +choose ``Add destination`` → ``Amazon S3``. Assign a name for your +output data and choose the S3 location where you want the data to be +stored and click Add destination button at the bottom as shown below. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/add-destination.png + + + + +This adds a destination node to our data flow. The destination node acts +as a sink to your data flow. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/destination.png + + + +Next, click on the Create job button in the upper right corner of the +page. In the configuration page for the SageMaker Processing job we are +about to create, choose the Instance type and Instance count for our +processing cluster. Advance configuration is optional, where you can +assign tags as needed and choose the appropriate Volume Size. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/configure-job.png + + + + +Select ``Run`` to start the export job. The job created will have Job +Name and Job ARN which can be used to search for the job status. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/create-job.png + + + + +The job created in the previous step will be available in the monitoring +page for ``SageMaker Processing job`` as shown in the figure below. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/processing-job.png + + + + +After a certain time, the job will be complete. The image below shows +the completed job. Exported data should be available in the output S3 +bucket. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/tabular-dataflow/complete-job.png + + + +This exported data can now be used for running the ML Models + + +Import Dataflow +---------------------------- + +Here is the final `Flow file +`__ available which you can directly import to expediate the process or validate the flow. + +Here are the steps to import the flow + +* Download the flow file + +* In Sagemaker Studio, drag and drop the flow file or use the upload button to browse the flow and upload diff --git a/sagemaker-datawrangler/timeseries-dataflow/TS-Workshop-DataPreparation.flow b/sagemaker-datawrangler/timeseries-dataflow/TS-Workshop-DataPreparation.flow new file mode 100644 index 0000000000..458de87e01 --- /dev/null +++ b/sagemaker-datawrangler/timeseries-dataflow/TS-Workshop-DataPreparation.flow @@ -0,0 +1,585 @@ +{ + "metadata": { + "version": 1, + "disable_limits": false, + "instance_type": "ml.m5.4xlarge" + }, + "parameters": [], + "nodes": [ + { + "node_id": "8e286fbf-3ad5-4683-92fe-7c2e6f5c69b6", + "type": "SOURCE", + "operator": "sagemaker.s3_source_0.1", + "parameters": { + "dataset_definition": { + "__typename": "S3CreateDatasetDefinitionOutput", + "datasetSourceType": "S3", + "name": "trip_data", + "description": null, + "s3ExecutionContext": { + "__typename": "S3ExecutionContext", + "s3Uri": "s3://768746145684-us-east-1-dw-ts-lab/trip data/", + "s3ContentType": "parquet", + "s3HasHeader": true, + "s3FieldDelimiter": ",", + "s3DirIncludesNested": false, + "s3AddsFilenameColumn": false + } + } + }, + "inputs": [], + "outputs": [ + { + "name": "default", + "sampling": { + "sampling_method": "sample_by_limit", + "limit_rows": 50000 + } + } + ] + }, + { + "node_id": "bf119511-56d6-4b73-9bc2-d9026ee3a10d", + "type": "TRANSFORM", + "operator": "sagemaker.spark.infer_and_cast_type_0.1", + "parameters": {}, + "trained_parameters": { + "schema": { + "store_and_fwd_flag": "string", + "VendorID": "long", + "tpep_pickup_datetime": "datetime", + "tpep_dropoff_datetime": "datetime", + "passenger_count": "long", + "trip_distance": "float", + "RatecodeID": "long", + "PULocationID": "long", + "DOLocationID": "long", + "payment_type": "long", + "fare_amount": "float", + "extra": "float", + "mta_tax": "float", + "tip_amount": "float", + "tolls_amount": "float", + "improvement_surcharge": "float", + "total_amount": "float", + "congestion_surcharge": "float", + "airport_fee": "float" + } + }, + "inputs": [ + { + "name": "default", + "node_id": "8e286fbf-3ad5-4683-92fe-7c2e6f5c69b6", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "ff490248-9beb-4bd9-8764-40409b93e59e", + "type": "TRANSFORM", + "operator": "sagemaker.spark.manage_columns_0.1", + "parameters": { + "operator": "Drop column", + "drop_column_parameters": { + "column_to_drop": [ + "VendorID", + "RatecodeID", + "store_and_fwd_flag", + "DOLocationID", + "payment_type", + "fare_amount", + "extra", + "mta_tax", + "tolls_amount", + "improvement_surcharge", + "passenger_count", + "congestion_surcharge", + "airport_fee" + ] + } + }, + "inputs": [ + { + "name": "df", + "node_id": "bf119511-56d6-4b73-9bc2-d9026ee3a10d", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "27cabb74-7b37-4bc8-8611-e3f36e3bbcda", + "type": "TRANSFORM", + "operator": "sagemaker.spark.time_series_0.1", + "parameters": { + "Validate timestamps_parameters": { + "timestamp_column": "tpep_pickup_datetime", + "policy": "drop" + }, + "operator": "Validate timestamps" + }, + "inputs": [ + { + "name": "df", + "node_id": "ff490248-9beb-4bd9-8764-40409b93e59e", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "33b52060-f318-4a5d-8fe3-895d703a025b", + "type": "TRANSFORM", + "operator": "sagemaker.spark.time_series_0.1", + "parameters": { + "Validate timestamps_parameters": { + "timestamp_column": "tpep_dropoff_datetime", + "policy": "drop" + }, + "operator": "Validate timestamps" + }, + "inputs": [ + { + "name": "df", + "node_id": "27cabb74-7b37-4bc8-8611-e3f36e3bbcda", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "3fc0ab7a-94b8-46ad-be39-7b9786206261", + "type": "TRANSFORM", + "operator": "sagemaker.spark.custom_code_0.1", + "parameters": { + "operator": "Python (PySpark)", + "pyspark_parameters": { + "code": "# Table is available as variable `df`\nfrom pyspark.sql.functions import col, round\ndf = df.withColumn('duration', round((col(\"tpep_dropoff_datetime\").cast(\"long\")-col(\"tpep_pickup_datetime\").cast(\"long\"))/60,2))\ndf = df.drop(\"tpep_dropoff_datetime\")" + }, + "name": "Duration_Transformation" + }, + "inputs": [ + { + "name": "df", + "node_id": "33b52060-f318-4a5d-8fe3-895d703a025b", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "6f1de2ca-cf98-48c3-9bde-026fbf66399a", + "type": "TRANSFORM", + "operator": "sagemaker.spark.handle_missing_0.1", + "parameters": { + "operator": "Fill missing", + "fill_missing_parameters": { + "input_column": [ + "PULocationID", + "tip_amount", + "total_amount" + ], + "fill_value": "0" + }, + "impute_parameters": { + "column_type": "Numeric", + "numeric_parameters": { + "strategy": "Approximate Median" + } + } + }, + "inputs": [ + { + "name": "df", + "node_id": "3fc0ab7a-94b8-46ad-be39-7b9786206261", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "5eb611f2-2cd0-4a04-bdd3-221ead204bbc", + "type": "TRANSFORM", + "operator": "sagemaker.spark.time_series_0.1", + "parameters": { + "Handle missing_parameters": { + "hm_input_type_Along column_parameters": { + "hmac_output_column": "", + "hmac_id_column": "PULocationID", + "hmac_strategy": "Constant Value", + "hmac_leftovernans": "Fill with Forward/Backward feed", + "hmac_strategy_Constant Value_parameters": { + "hmac_custom_value": "0.0" + }, + "hmac_sequence_column": "trip_distance", + "hmac_timestamp_column": "tpep_pickup_datetime" + }, + "hm_input_type": "Along column" + }, + "operator": "Handle missing" + }, + "inputs": [ + { + "name": "df", + "node_id": "6f1de2ca-cf98-48c3-9bde-026fbf66399a", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "4d6ac70b-97d9-4688-930d-988d63238d07", + "type": "TRANSFORM", + "operator": "sagemaker.spark.custom_code_0.1", + "parameters": { + "operator": "Python (PySpark)", + "pyspark_parameters": { + "code": "# Table is available as variable `df`\ndf = df.filter(df.trip_distance >= 0)\ndf = df.filter(df.tip_amount >= 0)\ndf = df.filter(df.total_amount >= 0)\ndf = df.filter(df.duration >= 1)\ndf = df.filter((1 <= df.PULocationID) & (df.PULocationID <= 263))\ndf = df.filter((df.tpep_pickup_datetime >= \"2019-01-01 00:00:00\") & (df.tpep_pickup_datetime < \"2020-03-01 00:00:00\"))" + } + }, + "inputs": [ + { + "name": "df", + "node_id": "5eb611f2-2cd0-4a04-bdd3-221ead204bbc", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "22d3a3bc-1d23-451d-8b2c-8be6e0e9fca0", + "type": "VISUALIZATION", + "operator": "sagemaker.visualizations.describe_0.1", + "parameters": { + "name": "Cleaned Dataset Summary" + }, + "inputs": [ + { + "name": "df", + "node_id": "4d6ac70b-97d9-4688-930d-988d63238d07", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "6681625c-0323-475a-8fac-443fe96da56a", + "type": "TRANSFORM", + "operator": "sagemaker.spark.handle_outliers_0.1", + "parameters": { + "operator": "Standard deviation numeric outliers", + "standard_deviation_numeric_outliers_parameters": { + "standard_deviations": 4, + "input_column": [ + "trip_distance", + "tip_amount", + "total_amount", + "duration" + ], + "fix_method": "Remove" + } + }, + "trained_parameters": { + "standard_deviation_numeric_outliers_parameters": [ + { + "_hash": "10b2dff28011403357a5a7dd4a37e9bea91721a8", + "lower_threshold": -12.393631771918914, + "upper_threshold": 18.423848932384466, + "input_column": "trip_distance" + }, + { + "_hash": "10b2dff28011403357a5a7dd4a37e9bea91721a8", + "lower_threshold": -8.711892476401351, + "upper_threshold": 12.165641974849178, + "input_column": "tip_amount" + }, + { + "_hash": "10b2dff28011403357a5a7dd4a37e9bea91721a8", + "lower_threshold": -29.770794210538003, + "upper_threshold": 58.2552896798414, + "input_column": "total_amount" + }, + { + "_hash": "10b2dff28011403357a5a7dd4a37e9bea91721a8", + "lower_threshold": -255.48697194000522, + "upper_threshold": 284.4440844902989, + "input_column": "duration" + } + ] + }, + "inputs": [ + { + "name": "df", + "node_id": "4d6ac70b-97d9-4688-930d-988d63238d07", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "31009b84-d667-427a-8649-a846f4370e99", + "type": "VISUALIZATION", + "operator": "sagemaker.visualizations.describe_0.1", + "parameters": { + "name": "Updated Table Summary" + }, + "inputs": [ + { + "name": "df", + "node_id": "6681625c-0323-475a-8fac-443fe96da56a", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "a16da785-dc80-44c4-b426-fdd68972a317", + "type": "TRANSFORM", + "operator": "sagemaker.spark.custom_code_0.1", + "parameters": { + "operator": "Python (PySpark)", + "pyspark_parameters": { + "code": "# Table is available as variable `df`\nfrom pyspark.sql.functions import col, date_trunc\ndf = df.withColumn('pickup_time', date_trunc(\"hour\",col(\"tpep_pickup_datetime\")))\ndf = df.drop(\"tpep_pickup_datetime\")" + } + }, + "inputs": [ + { + "name": "df", + "node_id": "6681625c-0323-475a-8fac-443fe96da56a", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "2d851ed9-6e82-409c-a10f-f2869d53b9f9", + "type": "TRANSFORM", + "operator": "sagemaker.spark.custom_code_0.1", + "parameters": { + "operator": "Python (PySpark)", + "pyspark_parameters": { + "code": "# Table is available as variable `df`\nfrom pyspark.sql import functions as f\nfrom pyspark.sql import Window\ndf = df.withColumn('count', f.count('duration').over(Window.partitionBy([f.col(\"pickup_time\"), f.col(\"PULocationID\")])))" + } + }, + "inputs": [ + { + "name": "df", + "node_id": "a16da785-dc80-44c4-b426-fdd68972a317", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "ce8c8a4a-8161-4615-b222-a5d1cda8d674", + "type": "TRANSFORM", + "operator": "sagemaker.spark.time_series_0.1", + "parameters": { + "Resample_parameters": { + "frequency": { + "quantity": 1, + "offset_description": "Hourly" + }, + "downsample": { + "non_numeric": "most common", + "numeric": "mean" + }, + "upsample": { + "non_numeric": "ffill", + "numeric": "linear" + }, + "timestamp_column": "pickup_time", + "id_column": "PULocationID" + }, + "operator": "Resample" + }, + "inputs": [ + { + "name": "df", + "node_id": "2d851ed9-6e82-409c-a10f-f2869d53b9f9", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "a7405baf-56f2-4ddc-8a7b-688f66e487da", + "type": "TRANSFORM", + "operator": "sagemaker.spark.time_series_0.1", + "parameters": { + "Featurize datetime_parameters": { + "output_column": "date", + "output_mode": "Ordinal", + "output_format": "Columns", + "infer_datetime_format": false, + "date_time_format": "", + "year": true, + "month": true, + "day": true, + "hour": true, + "minute": false, + "second": false, + "week_of_year": true, + "day_of_year": true, + "quarter": true, + "input_column": "pickup_time" + }, + "operator": "Featurize datetime" + }, + "inputs": [ + { + "name": "df", + "node_id": "ce8c8a4a-8161-4615-b222-a5d1cda8d674", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "51c5993e-56c5-43c6-8378-95c26037becd", + "type": "TRANSFORM", + "operator": "sagemaker.spark.time_series_0.1", + "parameters": { + "Lag features_parameters": { + "lag": 8, + "entire_window": true, + "drop_rows": false, + "entire_window_True_parameters": { + "flatten": true + }, + "sequence_column": "count", + "id_column": "PULocationID", + "timestamp_column": "pickup_time" + }, + "operator": "Lag features" + }, + "inputs": [ + { + "name": "df", + "node_id": "a7405baf-56f2-4ddc-8a7b-688f66e487da", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "85f7158d-64d9-4cbe-b965-b18364eca536", + "type": "TRANSFORM", + "operator": "sagemaker.spark.time_series_0.1", + "parameters": { + "Rolling window features_parameters": { + "window_size": 8, + "flatten": true, + "strategy": "Minimal subset", + "sequence_column": "count", + "timestamp_column": "pickup_time", + "id_column": "PULocationID" + }, + "operator": "Rolling window features" + }, + "inputs": [ + { + "name": "df", + "node_id": "51c5993e-56c5-43c6-8378-95c26037becd", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + }, + { + "node_id": "f157eb91-0cd7-4c04-a023-e1be3f343b2e", + "type": "DESTINATION", + "operator": "sagemaker.spark.s3_destination_0.1", + "name": "S3: NYC_Export", + "parameters": { + "output_config": { + "compression": "none", + "output_path": "s3://768746145684-us-east-1-dw-ts-lab/", + "output_content_type": "CSV", + "delimiter": "," + } + }, + "inputs": [ + { + "name": "default", + "node_id": "85f7158d-64d9-4cbe-b965-b18364eca536", + "output_name": "default" + } + ], + "outputs": [ + { + "name": "default" + } + ] + } + ] +} \ No newline at end of file diff --git a/sagemaker-datawrangler/timeseries-dataflow/index.rst b/sagemaker-datawrangler/timeseries-dataflow/index.rst new file mode 100644 index 0000000000..05cb0a9340 --- /dev/null +++ b/sagemaker-datawrangler/timeseries-dataflow/index.rst @@ -0,0 +1,1050 @@ +Timeseries Data Flow example +=================================================== +This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Timeseries dataset. + + + +New York city(NYC) yellow cab time-series example +------------------------------------------------- + +Our end goal for this lab is to prepare a time-series dataset and get it +to a ready state for ML modeling. We will start with the New York city +(NYC) yellow cab time-series dataset and work towards exploring, +preparing and transforming the dataset to help us design a ML model that +will predict the number of NYC yellow taxi pickups for any hour of the +day and location. As part of the exercise, we will learn how to derive +various insights about the trip like average tip value, average distance +for the trip, etc. + +Data used in this demo: - Original data source for all open data from +2008 to Current can be accessed here: +https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page - +AWS-hosted location: +https://registry.opendata.aws/nyc-tlc-trip-records-pds/ + +The taxi trip records include fields capturing pick-up and drop-off +dates/times, pick-up and drop-off locations, trip distances, itemized +fares, rate types, payment types, and driver-reported passenger counts. +The raw data is split 1 month per file, per Yellow, Green, or ForHire +from 2008 through 2020, with each file around 600 MB. The entire raw +dataset is huge. For our lab, we will use 13 of these files of around a +year’s worth of trip data and focus only on the iconic yellow cab trips. +We picked trip data from Feb 2019 to Feb 2020 to avoid COVID effects. +The data dictionary below describes the Yellow taxi trip data with raw +feature column names and their respective descriptions. The picked +dataset covers 13 months and encapsulates approximately 90 million +trips. + +Instructions to download the dateset +------------------------------------ + +You can use SageMaker Data Wrangler to import data from the following +data sources: Amazon Simple Storage Service (Amazon S3), Amazon Athena, +Amazon Redshift, and Snowflake. The dataset that you import can include +up to 1000 columns. For this lab, we will be using Amazon S3 as the +preferred data source. Before we import the dataset into SageMaker Data +Wrangler, let’s ensure we copy the dataset first from the publicly +hosted location to our local S3 bucket in our own account. + +To copy the dataset, copy and execute the Python code below within +SageMaker Studio. It is recommended to execute this code in a notebook +setting. We also recommend to have your S3 bucket in the same region as +SageMaker Data Wrangler. + +To create a SageMaker Studio notebook, from the launcher page, click on +the **Notebook Python 3** options under **Notebooks and compute +resources** as show in the figure below. + +Copy and paste the shared code snippet below into the launched +notebook’s cell (shown below) and execute it by clicking on the play +icon on the top bar. + +.. code:: python + + import boto3 + import json + + + # Setup + REGION = 'us-east-1' + account_id = boto3.client('sts').get_caller_identity().get('Account') + bucket_name = f'{account_id}-{REGION}-dw-ts-lab' + + + # Create S3 bucket to download dataset in your account + s3 = boto3.resource('s3') + + if REGION == 'us-east-1': + s3.create_bucket(Bucket=bucket_name) + else: + s3.create_bucket(Bucket=bucket_name,CreateBucketConfiguration={'LocationConstraint': REGION}) + # Copy dataset from public hosted location to your S3 bucket + trips = [ + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-02.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-03.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-04.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-05.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-06.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-07.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-08.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-09.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-10.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-11.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-12.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2020-01.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2020-02.parquet'} + ] + for trip in trips: + s3.meta.client.copy(trip, bucket_name, trip['Key']) + +Now we have raw data in our S3 bucket and ready to explore it and build +a training dataset + +Dataset Import +-------------- + +Our first step is to launch a new SageMaker Data Wrangler session and +there are multiple ways how to do that. For example, use the following: + +Click File -> New -> Data Wrangler Flow + +Amazon SageMaker will start to provision a resources for you and you a +could find a new Data Wrangler Flow file in a File Browser section + +Lets rename our new workflow: Right click on file -> Rename Data +Wrangler Flow + +Put a new name, for example: ``TS-Workshop-DataPreparation.flow`` + +In few minutes Data Wrangler will finish to provision resources and you +could see “Import Data” screen. SageMaker Data Wrangler supports many +data sources: Amazon S3, Amazon Athena, Amazon Redshift, Snowflake, +Databricks. Our data already in S3, let’s import it by clicking “Amazon +S3” button. + +You will see all your S3 buckets so please search for your bucket (if +you used provided code the bucket will have a suffix dw-ts-lab) + +All the files required for this lab are in “trip data” folder, so let’s +select it. SageMaker Data Wrangler will import all files from a folder +and sample up to 100 MB of data for an interactive preview. On a right +side menu you could customize import job settings like Name, File type, +Delimiter, etc. More information about import process could be found +`here `__. + +To finish setting up import step select “parquet” in “File type” drop +down menu and press the orange button “Import” + +It will take a few minutes to import data and validate it. SageMaker +Data Wrangler will automatically recognize data types. You should see +“Validation complete 0 errors message” + +Change data types +------------------ + +First we will check the data types were correctly recognized. This might +be necessary as Data Wrangler selects data types based on a sampled data +which is limited to 50000 rows. Sampled data might potentially miss some +variations. + +To add a data transformation step use the plus sign next to Data types +and choose Edit data types as shown below. + +In our case several columns were incorrectly recognized: - +``passenger_count`` (must be ``long`` instead of ``float``) - +``RatecodeID`` (must be ``long`` instead of ``float``) - ``airport_fee`` +(must be ``float`` instead of ``long``) + +I know correct data types from dataset description. In real life you +could also easily find such information. Let’s correct data types by +selecting a correct type from a drop down menu. + +Click Preview and then Apply button. + +Click Back to data flow. + +Dataset preparation +------------------- + +Drop columns +------------ + +Before we analyze data and do feature engineering we have to clean +dataset and below steps show how to remove unwanted data. + +To re-iterate our business goal: Predict the number of NY City yellow +taxi pickups in the next 24 hour for each pickup per hour zones and +provide some insights for drivers like average tips, average distance, +etc. + +As we are interested in per hour forecast we have to aggregate some +features and remove features which are impossible to aggregate. For this +purpose we don’t need the following columns: + +1. ``VendorID`` (A code indicating the TPEP provider that provided the + record) +2. ``RatecodeID`` (The final rate code in effect at the end of the + trip) +3. ``Store_and_fwd_flag`` (This flag indicates whether the trip record + was held in vehicle memory before sending to the vendor, aka “store + and forward,”because the vehicle did not have a connection to the + server) +4. ``DOLocationID`` (TLC Taxi Zone in which the taximeter was + disengaged) +5. ``Payment_type`` (A numeric code signifying how the passenger paid + for the trip) +6. ``Fare_amount`` (The time-and-distance fare calculated by the meter) + - we will use total amount feature +7. ``Extra`` (Miscellaneous extras and surcharges) +8. ``MTA_tax`` (0.50 MTA tax that is automatically triggered based on + the metered rate in use) +9. ``Tolls_amount`` (Total amount of all tolls paid in trip) +10. ``Improvement_surcharge`` (improvement surcharge assessed trips at + the flag drop. The improvement surcharge began being levied in 2015) +11. ``Passenger_count`` (This is a driver-entered value) +12. ``congestion_surcharge`` (Total amount collected in trip for NYS + congestion surcharge) +13. ``Airport_fee`` (Only at LaGuardia and John F. Kennedy Airports) + +To remove those columns: 1. Click the plus sign next to “Data types” +element and choose Add transform. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/SelectAddTransform.png + + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. | Choose Manage columns. + +4. For Transform, choose Drop column and for Column to drop, choose all + mentioned above. + +5. Choose Preview + +6. Choose Add to save the step. + +Once transformation is applied on a sampled data you should see all +current steps and a preview of a resulted dataset like show here. + +Click Back to data flow. + +Handle missing and invalid data in timestamps +--------------------------------------------- + +Missing data is a common problem in real life, it could be a result of +data corruption, data loss or issues in data ingestion. The best +practice is to verify the presence of any missing or invalid values and +handle them appropriately. + +There are many different strategies how missing or invalid data could be +handled, for example dropping rows with missing values or filling the +missing values with static or calculated values. Depending on dataset +size you could choose what to do: fix values or just drop them. The +**Time Series - Handle missing** transform allows you to choose from +multiple strategies. + +All future aggregations will be based on time stamps, so we have to make +sure that we don’t have any rows with missing time stamps ( +``tpep_pickup_datetime`` and ``tpep_dropoff_datetime`` features). +SageMaker Data Wrangler has several time series specific +transformations, including **Validate timestamps** which checks for +scenarios: 1. Checking timestamp column for any missing values. 2. +Validate the timestamp columns for the desired timestamp format. + +To validate timestamps in ``tpep_dropoff_datetime`` and +``tpep_pickup_datetime`` columns: 1. Click the plus sign next to “Drop +columns” element and choose Add transform. + + + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/AddDateValidationTransform.png + + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Time Series. + +4. For Transform choose Validate Timestamps, For TimeStamp columns + choose ``tpep_pickup_datetime``, for Policy select drop. + +5. Choose Preview + +6. Choose Add to save the step. + +7. Repeat same steps again for ``tpep_dropoff_datetime`` column + +When you apply a transformation a sampled data you should see all +current steps and a preview of a resulted dataset. + +Click Back to data flow. + +Feature engineering based on a timestamp with a custom transformation. +---------------------------------------------------------------------- + +At this stage we have pickup and drop-off timestamps, but we are more +interested in pickup timestamp and ride duration. We have to create a +new feature ``ride duration`` as a difference between pick up and drop +off time in minutes. There is no built-in date difference transformation +in a Data Wrangler, but we could create it with a custom transformation. +The **Custom Transforms** allows you to use Pyspark, Pandas, or Pyspark +(SQL) to define your own transformations. For all three options, you use +the variable ``df`` to access a dataframe to which you want to apply the +transform. You do not need to include a return statement. + +To create a custom transformation you have to: 1. Click the plus sign +next to a collection of transformation elements and choose Add +transform. + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Custom transform. + +4. Name the transformation as “Duration_Transformation” - (naming is + optional but good to have a structure) + +5. In drop down menu select Python (PySpark) and use code below. This + code will import functions, calculate difference between two + timestamps by converting them to unix format (real number) and round + result and drop tpep_dropoff_datetime column + + .. code:: python + + from pyspark.sql.functions import col, round + df = df.withColumn('duration', round((col("tpep_dropoff_datetime").cast("long")-col("tpep_pickup_datetime").cast("long"))/60,2)) + df = df.drop("tpep_dropoff_datetime") + +6. Choose Preview + +7. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all +current steps and a preview of a resulted dataset with a new column +duration and without column tpep_dropoff_datetime + +Click Back to data flow. + +Handling missing data in numeric attributes +------------------------------------------- + +We already discussed what are missing values and why it is important to +handle them. So far, we have been working with timestamps only. Now, we +are going to handle missing values in the rest of attributes. We can +exclude ``duration`` feature from this operation as it was calculated +from timestamps in the previous step. As we discussed before, there are +several ways to handle missing data: fill a static number or calculate a +value (for example: median or mean for last 7 days). It might make sense +to calculate a value if your time-series represents a continuous process +like sensor reading or product sale quantity. In our case, all trips are +independent from each other and we cannot calculate values based on +previous trips as it might bring data bias and increase error. We can +replace missing values with zeros or sometimes it might make sense to +drop the entire row with missing values. + +Amazon Data Wrangler has two types of transformations to handle missing +data: i) generic and ii) specifically designed for time series data. +Here, we demonstrate how to use both of them and describe when to use +each of these transformations. + +Handle missing data with the generic “Handle missing values” transformations +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This transformation can be used if you want to: 1. Replace missing +values with a same static value for all time series 2. Replace missing +values with a calculated value and you have only one time series (for +example: one sensor or one product in a shop) + +To create this transformation you have to: 1. Click the plus sign next +to a collection of transformation elements and choose Add transform. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/AddTransformMissingGeneral.png + + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Handle Missing + +4. For “Transform” choose Fill missing + +5. For “inputs columns” choose ``PULocationID``, ``tip_amount``, and + ``total_amount`` + +6. For “Fill value” put 0 + +7. Choose Preview + +8. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all +current steps and a preview of a resulted dataset. + +Handle missing data with special Time Series transformation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In real life datasets, we have many time-series in the same dataset and +to separate them, we use some form of IDs. For example, sensor ID or +item SKU. If we want to replace missing values with calculated values, +for example mean for last 10 sensor observations, we must calculate it +based on data for each time series independently. Instead of writing +code, you could use the special Time Series transformation in Data +Wrangler and get this easily done!. + +To create this transformation you have to: 1. Click “+ Add step” orange +button in the TRANSFORMS menu. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/AddStep.png + + +2. Choose Time Series + +3. For “Transform” choose Handle missing + +4. For “Time series input type” choose Along column + +5. For “Impute missing values for this column” choose ``trip_distance`` + +6. For “Timestamp column” choose tpep_pickup_datetime + +7. For “ID column” choose PULocationID + +8. For “Method for imputing values” choose Constant value + +9. For “Custom value” put 0.0 + +10. Choose Preview + +11. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all +current steps until this point in time and get a preview of the +resulting dataset. + +Filter rows with invalid data +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Based on our understanding of the dataset until this point, we could +also apply several filters to remove invalid or corrupt data from a +business point of view. This will improve data quality even further and +ensure we feed only correct data to our model training process. + +We can filter data based on following rules: 1. ``tpep_pickup_datetime`` +- have to be in range from 1 Jan 2019 (included) till 1 March 2020 +(excluded) 2. ``trip_distance`` - have to be greater than or equal to 0 +(only positive numbers) 3. ``tip_amount`` - have to be greater than or +equal to 0 (only positive numbers) 4. ``total_amount`` - have to be +greater than or equal to 0 (only positive numbers) 5. ``duration`` - +have to be greater than or equal to 1 (we are not interested in super +short trips). 6. ``PULocationID`` - have to be in the range (1 to 263). +These are the assigned zones. For the sake of brevity, let’s use only +the 1st ten location IDs for this workshop (see image below). + +There is no built-in filter transformation in Data Wrangler to handle +these various constraints. Hence, we will create a custom +transformation. + +To create a custom transformation, follow the steps below: 1. Click the +plus sign next to a collection of transformation elements and choose Add +transform. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/AddTransformFilter.png + + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Custom Transform. + +4. In drop down menu select Python (PySpark) and use code below. This + code will filter rows based on the specified conditions. + + .. code:: python + + df = df.filter(df.trip_distance >= 0) + df = df.filter(df.tip_amount >= 0) + df = df.filter(df.total_amount >= 0) + df = df.filter(df.duration >= 1) + df = df.filter((1 <= df.PULocationID) & (df.PULocationID <= 263)) + df = df.filter((df.tpep_pickup_datetime >= "2019-01-01 00:00:00") & (df.tpep_pickup_datetime < "2020-03-01 00:00:00")) + +5. Choose Preview + +6. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all +current steps until this point in time and get a preview of the +resulting dataset. + +Quick analysis of dataset +------------------------- + +Amazon SageMaker Data Wrangler includes built-in analysis that help you +generate visualizations and data insights in a few clicks. You can +either leverage the built-in analyses types we offer out of the box with +the product or create your own custom analysis using your own code if +needed. SageMaker Data Wrangler also provides automated insights by +automatically performing exploratory and descriptive analyses behind the +scenes on your data. It identifies hidden anomalies and red flags within +your dataset and proposes prescriptive actions in the form of what +transforms can be applied on what columns of your data to fix these +issues. + +For this lab, let’s use the Table Summary built-in analysis type to +quickly summarize our existing dataset in its current form. For the +numeric columns, including long and float data, table summary reports +the number of entries (``count``), minimum (``min``), maximum (``max``), +mean, and standard deviation (``stddev``) for each column. For columns +with non-numerical data, including columns with String, Boolean, or +DateTime data, table summary reports the number of entries (``count``), +least frequent value (``min``), and most frequent value (``max``). + +To create this analysis, follow the steps below: 1. Click the plus sign +next to a collection of transformation elements and choose “Add +analyses”. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/addFirstAnalyses.png + + +2. In a “analyses type” drop down menu select “Table Summary” and + provide a name for “Analysis name”, for example: “Cleaned dataset + summary” + +3. Choose Preview + +4. Choose Add to save the analyses. + +5. You could find your first analyses on a “Analysis” tab. All future + visualizations will could be also found here. + +6. Click on analyses icon to open it. + +Let’s take a look at our results. The most interesting part is the +summary for duration column: maximum value is 1439 and this is in +minutes! 1439 minutes = almost 24 hours and this is definitely an issue +which will reduce the quality of our model if this dataset is used in +its current form. This looks more like an issue due to the prevalence of +outliers in our dataset. Next, let’s see how to issue this issue using a +built-in transform Data Wrangler offers. + +Handling outliers in numeric attributes +--------------------------------------- + +In statistics, an outlier is a data point that differs significantly +from other observations in the same dataset. An outlier may be due to +variability in the measurement or it may indicate experimental error. +The latter are sometimes excluded from the dataset. For example, in our +dataset we have the ``tip_amount`` feature and usually it is less than +10 dollars, but due to an error in a data collection, some values can +show thousands of dollar as a tip. Such data errors will skew statistics +and aggregated values which will lead to a lower model accuracy. + +An outlier can cause serious problems in statistical analysis. Machine +learning models are sensitive to the distribution and range of feature +values. Outliers, or rare values, can negatively impact model accuracy +and lead to longer training times. When you define a Handle outliers +transform step, the statistics used to detect outliers are generated on +the data available in Data Wrangler when defining this step. These same +statistics are used when running a Data Wrangler job. + +SageMaker Data Wrangler supports several outliers detection and handle +methods. We are going to use **Standard Deviation Numeric Outliers** and +we remove all outliers as our dataset is big enough. This transform +detects and fixes outliers in numeric features using the mean and +standard deviation. You specify the number of standard deviations a +value must vary from the mean to be considered an outlier. For example, +if you specify 3 for standard deviations, a value falling more than 3 +standard deviations from the mean is considered an outlier. + +To create this transformation, follow the steps below: 1. Click the plus +sign next to a collection of transformation elements and choose “Add +transform”. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/AddTransformOutliers.png + + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Handle Outliers. + +4. For “Transform” choose “Standard deviation numeric outliers” + +5. For “Inputs columns” choose ``tip_amount``, ``total_amount``, + ``duration``, and ``trip_distance`` + +6. For “Fix method” choose “Remove” + +7. For “Standard deviations” put 4 + +8. Choose Preview + +9. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all +current steps and a preview of resulted dataset. + +Optional: If you want, you could repeat the steps from our previous +analysis (“Quick analysis of a current dataset”) to create a new table +summary and check for the new maximum for the ``duration`` column. You +can see, the new max value for duration is 243 minutes = just over an +hour. This is more realistic for long trips than what we previously had. + +Grouping/Aggregating data +------------------------- + +At this moment we have cleaned dataset by removing outliers, invalid +values, and added new features. There are few more steps before we start +training our forecasting model. + +As we are interested in a hourly forecast we have to count number of +trips per hour per station and also aggregate (with mean) all metrics +such as distance, duration, tip, total amount. + +Truncating timestamp +~~~~~~~~~~~~~~~~~~~~ + +We don’t need minutes and seconds in out timestamp, so we remove them. +There is no built-in filter transformation in SageMaker Data Wrangler, +so we create a custom transformation. + +To create a custom transformation, follow the steps below:: 1. Click the +plus sign next to a collection of transformation elements and choose +“Add transform”. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/addTrandformDate.png + + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Custom Transform. + +4. In drop down menu select Python (PySpark) and use code below. This + code will create a new column with a truncated timestamp and then + drop original pickup column. + + .. code:: python + + from pyspark.sql.functions import col, date_trunc + df = df.withColumn('pickup_time', date_trunc("hour",col("tpep_pickup_datetime"))) + df = df.drop("tpep_pickup_datetime") + +5. Choose Preview + +6. Choose Add to save the step + +When you apply the transformation on sampled data, you can see all the +current steps until this point in time and get a preview of the +resulting dataset with a new column ``pickup_time`` and without the old +column ``tpep_pickup_datetime`` + +Count number of trips per hour per station +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Currently, we have only piece of information about each trip, but we +don’t know how many trips were made from each station per hour. The +simplest way to do that is count number of records per stationID per +hourly timestamp. While Amazon Data Wrangler provides GroupBy +transformation. The built-in transformation doesn’t support grouping by +multiple columns, so we use a custom transformation. + +To create a custom transformation you have to: 1. Click the plus sign +next to a collection of transformation elements and choose “Add +transform”. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/addTrandformDate.png + + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Custom Transform. + +4. In drop down menu select Python (PySpark) and use code below. This + code will create a new column with a number of trips from each + location for each timestamp. + + .. code:: python + + from pyspark.sql import functions as f + from pyspark.sql import Window + df = df.withColumn('count', f.count('duration').over(Window.partitionBy([f.col("pickup_time"), f.col("PULocationID")]))) + +5. Choose Preview + +6. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all +current steps and a preview of a resulted dataset with a new column +count. + +Resample time series +-------------------- + +Now, we are ready to make a final aggregation! We want to aggregate all +rows by a combination of ``PULocationID`` and ``pickup_time`` columns, +while features should be replaced by mean value for each combination. + +We use special built-in Time Series transformation **Resample**. The +Resample transformation changes the frequency of the time series +observations to a specified granularity. It also comes with both +upsampling and downsampling options. Applying upsampling increases the +frequency of the observations, for example from daily to hourly, whereas +downsampling decreases the frequency of the observations, for example +from hourly to daily. + +To create this transformation, follow the steps below: 1. Click the plus +sign next to a collection of transformation elements and choose Add +transform. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/AddResample.png + + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Time Series. + +4. For “Transform” choose “Resample” + +5. For “Timestamp” choose ``pickup_time`` + +6. For “ID column” choose ``PULocationID`` + +7. For “Frequency unit” choose “Hourly” + +8. For “Frequency quantity” put 1 + +9. For “Method to aggregate numeric values” choose “mean” + +10. Use default values for the rest of parameters + +11. Choose Preview + +12. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all +current steps and a preview of a resulted dataset. + +.. _resample-time-series-1: + +Resample time series +-------------------- + +Now we are ready to make a final aggregation! We aggregate all rows by +combination of ``PULocationID`` and ``pickup_time`` timestamp while +features should be replaced by mean value for each combination. + +We use special built-in Time Series transformation **Resample**. The +Resample transformation changes the frequency of the time series +observations to a specified granularity. It also comes with both +upsampling and downsampling options. Applying upsampling increases the +frequency of the observations, for example from daily to hourly, whereas +downsampling decreases the frequency of the observations, for example +from hourly to daily. + +To create this transformation you have to: 1. Click the plus sign next +to a collection of transformation elements and choose Add transform. + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/AddResample.png + + + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Time Series. + +4. For “Transform” choose “Resample” + +5. For “Timestamp” choose pickup_time + +6. For “ID column” choose “PULocationID” + +7. For “Frequency unit” choose “Hourly” + +8. For “Frequency quantity” put 1 + +9. For “Method to aggregate numeric values” choose “mean” + +10. Use default values for the rest of parameters + +11. Choose Preview + +12. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all +current steps until this point in time and get a preview of the +resulting dataset. + +`here `__. + +Featurize Date Time +------------------- + +“Featurize datetime” time series transformation will add the month, day +of the month, day of the year, week of the year, hour and quarter +features to our dataset. Because we’re providing the date/time +components as separate features, we enable ML algorithms to detect +signals and patterns for improving prediction accuracy. + +To create this transformation you have to: 1. Click the plus sign next +to a collection of transformation elements and choose Add transform + +2. Click “+ Add step” orange button in the TRANSFORMS menu + +3. Choose Time Series + + - For “Transform” choose “Featurize date/time” + + - For “Input Column” choose ``pickup_time`` + + - For “Output Column” enter “date” + + - For “Output mode” choose “Ordinal” + + - For “Output format” choose “Columns” + + - For date/time features to extract, select Year, Month, Day, Hour, + Week of year, Day of year, and Quarter. + +4. Choose Preview + +5. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all +current steps until this point in time and get a preview of the +resulting dataset. + +Click “Back to data flow” to head back to the block diagram editor +window. + +Lag feature +----------- + +Next let’s create lag features for the target column count. Lag features +in time-series analysis are values at prior timestamps that are +considered helpful in inferring future values. They also help identify +**autocorrelation**, also known as serial correlation, patterns in the +residual series by quantifying the relationship of the observation with +observations at previous time steps. Autocorrelation is similar to +regular correlation but between the values in a series and its past +values. It forms the basis for the **autoregressive forecasting** models +in the **ARIMA** series. + +With SageMaker Data Wrangler’s Lag feature transform, you can easily +create lag features n periods apart. Additionally, we often want to +create multiple lag features at different lags and let the model decide +the most meaningful features. For such a scenario, the **Lag features** +transform helps create multiple lag columns over a specified window +size. + +To create this transformation, follow the steps below: 1. Click the plus +sign next to a collection of transformation elements and choose Add +transform. + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Time Series + + - For “Transform” choose “Lag features” + + - For “Generate lag features for this column” choose “count” + + - For “ID column” enter “PULocationID” + + - For “Timestamp Column” choose “pickup_time” + + - For Lag, enter 8. You could try to use different values, maybe 24 + hours in our case makes more sense. + + - Because we’re interested in observing up to the previous 8 lag + values, let’s select Include the entire lag window. + + - To create a new column for each lag value, select Flatten the + output + +4. Choose Preview + +5. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all +current steps and a preview of a resulted dataset. + +Rolling window features +----------------------- + +We can also calculate meaningful statistical summaries across a range of +values and include them as input features. Let’s extract common +statistical time series features. + +Data Wrangler implements automatic time series feature extraction +capabilities using the open source ``tsfresh`` package. With the time +series feature extraction transforms, you can automate the feature +extraction process. This eliminates the time and effort otherwise spent +manually implementing signal processing libraries. We will extract +features using the **Rolling window** features transform. This method +computes statistical properties across a set of observations defined by +the window size. + +To create this transformation you have to: 1. Click the plus sign next +to a collection of transformation elements and choose Add transform + +2. Click “+ Add step” orange button in the TRANSFORMS menu. + +3. Choose Time Series + + - For “Transform” choose “Rolling window features” + + - For “Generate rolling window features for this column” choose + “count” + + - For “Timestamp Column” choose “pickup_time” + + - For “ID column” enter ``PULocationID`` + + - For “Window size”, enter 8. You could try to use different values, + maybe 24 hours in our case makes more sense. + + - Select Flatten to create a new column for each computed feature. + + - Choose “Strategy” as “Minimal subset”. This strategy extracts + eight features that are useful in downstream analyses. Other + strategies include Efficient Subset, Custom subset, and All + features. + +4. Choose Preview + +5. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all +current steps until this point in time and get a preview of the +resulting dataset. + +Click “Back to data flow” to head back to the block diagram editor +window. + +Export Data +----------- + +At this stage, we have a new dataset that is cleaned and transformed +with newly engineered features. This dataset can be used for forecasting +either using open source libraries/frameworks or AWS services like +`Amazon SageMaker +Autopilot `__, `Amazon +SageMaker Canvas `__ or +`Amazon Forecast `__. + +Given, we had only used a sample of the dataset for creating our data +preparation and transformation recipe so far, what need to do next is to +apply the same recipe (data flow) on our entire dataset and scale the +whole process in a distributed fashion. Amazon Data Wrangler let’s you +do this in multiple ways. You can export your data flow: 1/ as a +processing job, 2/ as a SageMaker pipeline step, or 3/ as a Python +script. You can also kick-off these distributed jobs via the UI without +writing any code using Data Wrangler’s destination node option. The +export options are also facilitated via SageMaker Studio notebooks +(Jupyter). Additionally, the transformed features can also be ingested +directly to SageMaker Feature Store. + +For this lab, we will see how to use the destination nodes option to +export the transformed features to S3 via a distributed PySpark job +powered by SageMaker Processing. + +Exporting to S3 using Destination Nodes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This option creates a SageMaker processing job which uses the data flow +(recipe) we have created previously to kick-off a distributed processing +job on the “entire” dataset saving the results to a specified S3 bucket. + +Additionally, you can also drop columns if needed right before the +export step. For the sake of brevity and to simplify the prediction +problem statement, let’s drop all columns except three columns +``pickup_time``, ``count``, ``PULocationID``. Here count is the target +variable we will try to predict. ``pickup_time`` and ``PULocationID`` +will be our feature columns used for modeling. To create the model, we +will be using SageMaker Autopilot. This will be covered in the next 2 +sections. + +Follow the next steps to setup export to S3. 1. Click the plus sign next +to a collection of transformation elements and choose **“Add +destination” -> “Amazon S3”** + +.. figure:: https://s3.amazonaws.com/sagemaker-sample-files/images/sagemaker-datawrangler/timeseries-dataflow/addDestination.png + + +2. Provide parameters for S3 destination: + + - Dataset name - name for new dataset, for example used “NYC_export” + - File type - CSV + - Delimiter - Comma + - Compression - none + - Amazon S3 location - You can use the same bucket name which we + created at the beginning + +3. Click “Add destination” orange button + +4. Now your dataflow has a final step and you see a new “Create job” + orange button. Click it. + +5. Provide a “Job name” or keep autogenerated option and select + “destination”. We have only one “S3:NYC_export”, but you might have + multiple destinations from different steps in your workflow. Leave a + “KMS key ARN” field empty and click “Next” orange button. + +6. Now your have to provide configuration for a compute capacity for a + job. You can keep all defaults values: + + - For Instance type use “ml.m5.4xlarge” + - For Instance count use “2” + - You can explore “Additional configuration”, but keep them without + change. + - Click “Run” orange button + +7. Now your job is started and it takes about 1 hour to process 6 GB of + data according to our Data Wrangler processing flow. Cost for this + job will be around 2 USD as “ml.m5.4xlarge” cost 0.922 USD per hour + and we are using two of them. + +8. If you click on the job name you will be redirected to a new window + with the job details. On the job details page you see all parameters + from a previous steps. + +Approximately in one hour you should see that job status changed to +“Completed” and you could also check “Processing time (seconds)” value. + +Now you could close job details page. + +Check Processed output +---------------------- + +After the SageMaker Data Wrangler processing job is completed, we can +check the results saved in our destination S3 bucket. + +At this stage, you have designed a data flow for data processing and +feature engineering and successfully launched it. Of course it is not +mandatory to always run a job by clicking on the “Run” button. You could +also automate it, but this is a topic of another workshop in this +series! + +.. container:: alert alert-info + + 💡 Congratulations! You reached the end of this part. Now you know + how to use Amazon SageMaker Data Wrangler for time series dataset + preparation! + + :: + +Import Dataflow +---------------------------- + +Here is the final `Flow file +`__ available which you can directly import to expediate the process or validate the flow. + +Here are the steps to import the flow + +* Download the flow file + +* In Sagemaker Studio, drag and drop the flow file or use the upload button to browse the flow and upload + + +Clean up +----------------------------------------------------- + +- Delete artifacts in S3. +- Delete data flow file in SageMaker Studio. +- Stop active SageMaker Data Wrangler instance. +- Delete SageMaker user profile and domain (optional). diff --git a/sagemaker-datawrangler/timeseries-dataflow/readme.md b/sagemaker-datawrangler/timeseries-dataflow/readme.md new file mode 100644 index 0000000000..21ad358746 --- /dev/null +++ b/sagemaker-datawrangler/timeseries-dataflow/readme.md @@ -0,0 +1,780 @@ +# Amazon SageMaker Data Wrangler time series workshop + +Our end goal for this lab is to prepare a time-series dataset and get it to a ready state for ML modeling. We will start with the New York city (NYC) yellow cab time-series dataset and work towards exploring, preparing and transforming the dataset to help us design a ML model that will predict the number of NYC yellow taxi pickups for any hour of the day and location. As part of the exercise, we will learn how to derive various insights about the trip like average tip value, average distance for the trip, etc. + +Data used in this demo: +- Original data source for all open data from 2008 to Current can be accessed here: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page +- AWS-hosted location: https://registry.opendata.aws/nyc-tlc-trip-records-pds/ + +The taxi trip records include fields capturing pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts. The raw data is split 1 month per file, per Yellow, Green, or ForHire from 2008 through 2020, with each file around 600 MB. The entire raw dataset is huge. For our lab, we will use 13 of these files of around a year's worth of trip data and focus only on the iconic yellow cab trips. We picked trip data from Feb 2019 to Feb 2020 to avoid COVID effects. The data dictionary below describes the Yellow taxi trip data with raw feature column names and their respective descriptions. The picked dataset covers 13 months and encapsulates approximately 90 million trips. + + + + +## Instructions to download the dateset +You can use SageMaker Data Wrangler to import data from the following data sources: Amazon Simple Storage Service (Amazon S3), Amazon Athena, Amazon Redshift, and Snowflake. The dataset that you import can include up to 1000 columns. For this lab, we will be using Amazon S3 as the preferred data source. Before we import the dataset into SageMaker Data Wrangler, let's ensure we copy the dataset first from the publicly hosted location to our local S3 bucket in our own account. + +To copy the dataset, copy and execute the Python code below within SageMaker Studio. It is recommended to execute this code in a notebook setting. We also recommend to have your S3 bucket in the same region as SageMaker Data Wrangler. + +To create a SageMaker Studio notebook, from the launcher page, click on the ***Notebook Python 3*** options under ***Notebooks and compute resources*** as show in the figure below. + + + +Copy and paste the shared code snippet below into the launched notebook's cell (shown below) and execute it by clicking on the play icon on the top bar. + + + + +```python +import boto3 +import json + + +# Setup +REGION = 'us-east-1' +account_id = boto3.client('sts').get_caller_identity().get('Account') +bucket_name = f'{account_id}-{REGION}-dw-ts-lab' + + +# Create S3 bucket to download dataset in your account +s3 = boto3.resource('s3') + +if REGION == 'us-east-1': + s3.create_bucket(Bucket=bucket_name) +else: + s3.create_bucket(Bucket=bucket_name,CreateBucketConfiguration={'LocationConstraint': REGION}) + # Copy dataset from public hosted location to your S3 bucket +trips = [ + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-02.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-03.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-04.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-05.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-06.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-07.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-08.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-09.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-10.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-11.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2019-12.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2020-01.parquet'}, + {'Bucket': 'nyc-tlc', 'Key': 'trip data/yellow_tripdata_2020-02.parquet'} +] +for trip in trips: + s3.meta.client.copy(trip, bucket_name, trip['Key']) +``` + +Now we have raw data in our S3 bucket and ready to explore it and build a training dataset + +## Dataset Import + +Our first step is to launch a new SageMaker Data Wrangler session and there are multiple ways how to do that. For example, use the following: + +Click File -> New -> Data Wrangler Flow + + + +Amazon SageMaker will start to provision a resources for you and you a could find a new Data Wrangler Flow file in a File Browser section + + + +Lets rename our new workflow: Right click on file -> Rename Data Wrangler Flow + + + +Put a new name, for example: `TS-Workshop-DataPreparation.flow` + + + +In few minutes Data Wrangler will finish to provision resources and you could see "Import Data" screen. +SageMaker Data Wrangler supports many data sources: Amazon S3, Amazon Athena, Amazon Redshift, Snowflake, Databricks. +Our data already in S3, let's import it by clicking "Amazon S3" button. + + + + +You will see all your S3 buckets so please search for your bucket (if you used provided code the bucket will have a suffix dw-ts-lab) + + + +All the files required for this lab are in "trip data" folder, so let's select it. SageMaker Data Wrangler will import all files from a folder and sample up to 100 MB of data for an interactive preview. On a right side menu you could customize import job settings like Name, File type, Delimiter, etc. More information about import process could be found [here](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-import.html). + + +To finish setting up import step select "parquet" in "File type" drop down menu and press the orange button "Import" + + + + +It will take a few minutes to import data and validate it. SageMaker Data Wrangler will automatically recognize data types. You should see "Validation complete 0 errors message" + + + + +# Change data types + +First we will check the data types were correctly recognized. This might be necessary as Data Wrangler selects data types based on a sampled data which is limited to 50000 rows. Sampled data might potentially miss some variations. + + +To add a data transformation step use the plus sign next to Data types and choose Edit data types as shown below. + + + + +In our case several columns were incorrectly recognized: +- `passenger_count` (must be `long` instead of `float`) +- `RatecodeID` (must be `long` instead of `float`) +- `airport_fee` (must be `float` instead of `long`) + +I know correct data types from dataset description. In real life you could also easily find such information. Let's correct data types by selecting a correct type from a drop down menu. + + + + +Click Preview and then Apply button. + +Click Back to data flow. + +# Dataset preparation +## Drop columns + +Before we analyze data and do feature engineering we have to clean dataset and below steps show how to remove unwanted data. + +To re-iterate our business goal: +Predict the number of NY City yellow taxi pickups in the next 24 hour for each pickup per hour zones and provide some insights for drivers like average tips, average distance, etc. + +As we are interested in per hour forecast we have to aggregate some features and remove features which are impossible to aggregate. For this purpose we don't need the following columns: + +1. `VendorID` (A code indicating the TPEP provider that provided the record) +2. `RatecodeID` (The final rate code in effect at the end of the trip) +3. `Store_and_fwd_flag` (This flag indicates whether the trip record was held in vehicle memory before sending to the vendor, aka “store and forward,”because the vehicle did not have a connection to the server) +4. `DOLocationID` (TLC Taxi Zone in which the taximeter was disengaged) +5. `Payment_type` (A numeric code signifying how the passenger paid for the trip) +6. `Fare_amount` (The time-and-distance fare calculated by the meter) - we will use total amount feature +7. `Extra` (Miscellaneous extras and surcharges) +8. `MTA_tax` (0.50 MTA tax that is automatically triggered based on the metered rate in use) +9. `Tolls_amount` (Total amount of all tolls paid in trip) +10. `Improvement_surcharge` (improvement surcharge assessed trips at the flag drop. The improvement surcharge began being levied in 2015) +11. `Passenger_count` (This is a driver-entered value) +12. `congestion_surcharge` (Total amount collected in trip for NYS congestion surcharge) +13. `Airport_fee` (Only at LaGuardia and John F. Kennedy Airports) + +To remove those columns: +1. Click the plus sign next to "Data types" element and choose Add transform. + + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + + +3. Choose Manage columns. \ + + + + +4. For Transform, choose Drop column and for Column to drop, choose all mentioned above. + + + + +5. Choose Preview +6. Choose Add to save the step. + +Once transformation is applied on a sampled data you should see all current steps and a preview of a resulted dataset like show here. + + + +Click Back to data flow. + +## Handle missing and invalid data in timestamps +Missing data is a common problem in real life, it could be a result of data corruption, data loss or issues in data ingestion. The best practice is to verify the presence of any missing or invalid values and handle them appropriately. + +There are many different strategies how missing or invalid data could be handled, for example dropping rows with missing values or filling the missing values with static or calculated values. Depending on dataset size you could choose what to do: fix values or just drop them. The **Time Series - Handle missing** transform allows you to choose from multiple strategies. + +All future aggregations will be based on time stamps, so we have to make sure that we don't have any rows with missing time stamps ( `tpep_pickup_datetime` and `tpep_dropoff_datetime` features). SageMaker Data Wrangler has several time series specific transformations, including **Validate timestamps** which checks for scenarios: +1. Checking timestamp column for any missing values. +2. Validate the timestamp columns for the desired timestamp format. + +To validate timestamps in `tpep_dropoff_datetime` and `tpep_pickup_datetime` columns: +1. Click the plus sign next to "Drop columns" element and choose Add transform. + + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Time Series. + + + +4. For Transform choose Validate Timestamps, For TimeStamp columns choose `tpep_pickup_datetime`, for Policy select drop. + + + +5. Choose Preview +6. Choose Add to save the step. +7. Repeat same steps again for `tpep_dropoff_datetime` column + +When you apply a transformation a sampled data you should see all current steps and a preview of a resulted dataset. + + + +Click Back to data flow. + + +## Feature engineering based on a timestamp with a custom transformation. + +At this stage we have pickup and drop-off timestamps, but we are more interested in pickup timestamp and ride duration. We have to create a new feature `ride duration` as a difference between pick up and drop off time in minutes. There is no built-in date difference transformation in a Data Wrangler, but we could create it with a custom transformation. The **Custom Transforms** allows you to use Pyspark, Pandas, or Pyspark (SQL) to define your own transformations. For all three options, you use the variable `df` to access a dataframe to which you want to apply the transform. You do not need to include a return statement. + +To create a custom transformation you have to: +1. Click the plus sign next to a collection of transformation elements and choose Add transform. + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Custom transform. + + + +4. Name the transformation as "Duration_Transformation" - (naming is optional but good to have a structure) +5. In drop down menu select Python (PySpark) and use code below. This code will import functions, calculate difference between two timestamps by converting them to unix format (real number) and round result and drop tpep_dropoff_datetime column + + ```Python + from pyspark.sql.functions import col, round + df = df.withColumn('duration', round((col("tpep_dropoff_datetime").cast("long")-col("tpep_pickup_datetime").cast("long"))/60,2)) + df = df.drop("tpep_dropoff_datetime") + ``` + + +6. Choose Preview +7. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all current steps and a preview of a resulted dataset with a new column duration and without column tpep_dropoff_datetime + + + +Click Back to data flow. + +## Handling missing data in numeric attributes + +We already discussed what are missing values and why it is important to handle them. So far, we have been working with timestamps only. Now, we are going to handle missing values in the rest of attributes. We can exclude `duration` feature from this operation as it was calculated from timestamps in the previous step. As we discussed before, there are several ways to handle missing data: fill a static number or calculate a value (for example: median or mean for last 7 days). It might make sense to calculate a value if your time-series represents a continuous process like sensor reading or product sale quantity. In our case, all trips are independent from each other and we cannot calculate values based on previous trips as it might bring data bias and increase error. We can replace missing values with zeros or sometimes it might make sense to drop the entire row with missing values. + +Amazon Data Wrangler has two types of transformations to handle missing data: i) generic and ii) specifically designed for time series data. Here, we demonstrate how to use both of them and describe when to use each of these transformations. + +### Handle missing data with the generic "Handle missing values" transformations + +This transformation can be used if you want to: +1. Replace missing values with a same static value for all time series +2. Replace missing values with a calculated value and you have only one time series (for example: one sensor or one product in a shop) + +To create this transformation you have to: +1. Click the plus sign next to a collection of transformation elements and choose Add transform. + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Handle Missing + + + +4. For "Transform" choose Fill missing + +5. For "inputs columns" choose `PULocationID`, `tip_amount`, and `total_amount` + +6. For "Fill value" put 0 + + + +7. Choose Preview +8. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all current steps and a preview of a resulted dataset. + + + +### Handle missing data with special Time Series transformation + +In real life datasets, we have many time-series in the same dataset and to separate them, we use some form of IDs. For example, sensor ID or item SKU. If we want to replace missing values with calculated values, for example mean for last 10 sensor observations, we must calculate it based on data for each time series independently. Instead of writing code, you could use the special Time Series transformation in Data Wrangler and get this easily done!. + +To create this transformation you have to: +1. Click "+ Add step" orange button in the TRANSFORMS menu. + + + + +2. Choose Time Series + + + +3. For "Transform" choose Handle missing + +4. For "Time series input type" choose Along column + +5. For "Impute missing values for this column" choose `trip_distance` + +6. For "Timestamp column" choose tpep_pickup_datetime + +7. For "ID column" choose PULocationID + +8. For "Method for imputing values" choose Constant value + +9. For "Custom value" put 0.0 + + + +10. Choose Preview +11. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all current steps until this point in time and get a preview of the resulting dataset. + + + +### Filter rows with invalid data + +Based on our understanding of the dataset until this point, we could also apply several filters to remove invalid or corrupt data from a business point of view. This will improve data quality even further and ensure we feed only correct data to our model training process. + +We can filter data based on following rules: +1. `tpep_pickup_datetime` - have to be in range from 1 Jan 2019 (included) till 1 March 2020 (excluded) +2. `trip_distance` - have to be greater than or equal to 0 (only positive numbers) +3. `tip_amount` - have to be greater than or equal to 0 (only positive numbers) +4. `total_amount` - have to be greater than or equal to 0 (only positive numbers) +5. `duration` - have to be greater than or equal to 1 (we are not interested in super short trips). +6. `PULocationID` - have to be in the range (1 to 263). These are the assigned zones. For the sake of brevity, let's use only the 1st ten location IDs for this workshop (see image below). + + + +There is no built-in filter transformation in Data Wrangler to handle these various constraints. Hence, we will create a custom transformation. + +To create a custom transformation, follow the steps below: +1. Click the plus sign next to a collection of transformation elements and choose Add transform. + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Custom Transform. + + + +4. In drop down menu select Python (PySpark) and use code below. This code will filter rows based on the specified conditions. + + ```Python + df = df.filter(df.trip_distance >= 0) + df = df.filter(df.tip_amount >= 0) + df = df.filter(df.total_amount >= 0) + df = df.filter(df.duration >= 1) + df = df.filter((1 <= df.PULocationID) & (df.PULocationID <= 263)) + df = df.filter((df.tpep_pickup_datetime >= "2019-01-01 00:00:00") & (df.tpep_pickup_datetime < "2020-03-01 00:00:00")) + ``` + + + + +5. Choose Preview +6. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all current steps until this point in time and get a preview of the resulting dataset. + + + +## Quick analysis of dataset + +Amazon SageMaker Data Wrangler includes built-in analysis that help you generate visualizations and data insights in a few clicks. You can either leverage the built-in analyses types we offer out of the box with the product or create your own custom analysis using your own code if needed. SageMaker Data Wrangler also provides automated insights by automatically performing exploratory and descriptive analyses behind the scenes on your data. It identifies hidden anomalies and red flags within your dataset and proposes prescriptive actions in the form of what transforms can be applied on what columns of your data to fix these issues. + +For this lab, let's use the Table Summary built-in analysis type to quickly summarize our existing dataset in its current form. For the numeric columns, including long and float data, table summary reports the number of entries (`count`), minimum (`min`), maximum (`max`), mean, and standard deviation (`stddev`) for each column. For columns with non-numerical data, including columns with String, Boolean, or DateTime data, table summary reports the number of entries (`count`), least frequent value (`min`), and most frequent value (`max`). + +To create this analysis, follow the steps below: +1. Click the plus sign next to a collection of transformation elements and choose "Add analyses". + + + +2. In a "analyses type" drop down menu select "Table Summary" and provide a name for "Analysis name", for example: "Cleaned dataset summary" + + + +3. Choose Preview + +4. Choose Add to save the analyses. + +5. You could find your first analyses on a "Analysis" tab. All future visualizations will could be also found here. + + + +6. Click on analyses icon to open it. + +Let's take a look at our results. The most interesting part is the summary for duration column: maximum value is 1439 and this is in minutes! 1439 minutes = almost 24 hours and this is definitely an issue which will reduce the quality of our model if this dataset is used in its current form. This looks more like an issue due to the prevalence of outliers in our dataset. Next, let's see how to issue this issue using a built-in transform Data Wrangler offers. + + + +## Handling outliers in numeric attributes + +In statistics, an outlier is a data point that differs significantly from other observations in the same dataset. An outlier may be due to variability in the measurement or it may indicate experimental error. The latter are sometimes excluded from the dataset. For example, in our dataset we have the `tip_amount` feature and usually it is less than 10 dollars, but due to an error in a data collection, some values can show thousands of dollar as a tip. Such data errors will skew statistics and aggregated values which will lead to a lower model accuracy. + +An outlier can cause serious problems in statistical analysis. Machine learning models are sensitive to the distribution and range of feature values. Outliers, or rare values, can negatively impact model accuracy and lead to longer training times. When you define a Handle outliers transform step, the statistics used to detect outliers are generated on the data available in Data Wrangler when defining this step. These same statistics are used when running a Data Wrangler job. + +SageMaker Data Wrangler supports several outliers detection and handle methods. We are going to use **Standard Deviation Numeric Outliers** and we remove all outliers as our dataset is big enough. This transform detects and fixes outliers in numeric features using the mean and standard deviation. You specify the number of standard deviations a value must vary from the mean to be considered an outlier. For example, if you specify 3 for standard deviations, a value falling more than 3 standard deviations from the mean is considered an outlier. + +To create this transformation, follow the steps below: +1. Click the plus sign next to a collection of transformation elements and choose "Add transform". + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Handle Outliers. + + + +4. For "Transform" choose "Standard deviation numeric outliers" +5. For "Inputs columns" choose `tip_amount`, `total_amount`, `duration`, and `trip_distance` +6. For "Fix method" choose "Remove" +7. For "Standard deviations" put 4 + + + +8. Choose Preview +9. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all current steps and a preview of resulted dataset. + + + + +Optional: If you want, you could repeat the steps from our previous analysis ("Quick analysis of a current dataset") to create a new table summary and check for the new maximum for the `duration` column. You can see, the new max value for duration is 243 minutes = just over an hour. This is more realistic for long trips than what we previously had. + + + +## Grouping/Aggregating data +At this moment we have cleaned dataset by removing outliers, invalid values, and added new features. There are few more steps before we start training our forecasting model. + +As we are interested in a hourly forecast we have to count number of trips per hour per station and also aggregate (with mean) all metrics such as distance, duration, tip, total amount. + +### Truncating timestamp +We don't need minutes and seconds in out timestamp, so we remove them. There is no built-in filter transformation in SageMaker Data Wrangler, so we create a custom transformation. + +To create a custom transformation, follow the steps below:: +1. Click the plus sign next to a collection of transformation elements and choose "Add transform". + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Custom Transform. + + + +4. In drop down menu select Python (PySpark) and use code below. This code will create a new column with a truncated timestamp and then drop original pickup column. + + ```Python + from pyspark.sql.functions import col, date_trunc + df = df.withColumn('pickup_time', date_trunc("hour",col("tpep_pickup_datetime"))) + df = df.drop("tpep_pickup_datetime") + ``` + + + +5. Choose Preview + +6. Choose Add to save the step + +When you apply the transformation on sampled data, you can see all the current steps until this point in time and get a preview of the resulting dataset with a new column `pickup_time` and without the old column `tpep_pickup_datetime` + + + +### Count number of trips per hour per station +Currently, we have only piece of information about each trip, but we don't know how many trips were made from each station per hour. The simplest way to do that is count number of records per stationID per hourly timestamp. While Amazon Data Wrangler provides GroupBy transformation. The built-in transformation doesn't support grouping by multiple columns, so we use a custom transformation. + +To create a custom transformation you have to: +1. Click the plus sign next to a collection of transformation elements and choose "Add transform". + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Custom Transform. + + + +4. In drop down menu select Python (PySpark) and use code below. This code will create a new column with a number of trips from each location for each timestamp. + + ```Python + from pyspark.sql import functions as f + from pyspark.sql import Window + df = df.withColumn('count', f.count('duration').over(Window.partitionBy([f.col("pickup_time"), f.col("PULocationID")]))) + ``` + + + +5. Choose Preview +6. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all current steps and a preview of a resulted dataset with a new column count. + + + +## Resample time series +Now, we are ready to make a final aggregation! We want to aggregate all rows by a combination of `PULocationID` and `pickup_time` columns, while features should be replaced by mean value for each combination. + +We use special built-in Time Series transformation **Resample**. The Resample transformation changes the frequency of the time series observations to a specified granularity. It also comes with both upsampling and downsampling options. Applying upsampling increases the frequency of the observations, for example from daily to hourly, whereas downsampling decreases the frequency of the observations, for example from hourly to daily. + +To create this transformation, follow the steps below: +1. Click the plus sign next to a collection of transformation elements and choose Add transform. + + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + + +3. Choose Time Series. + + + +4. For "Transform" choose "Resample" +5. For "Timestamp" choose `pickup_time` +6. For "ID column" choose `PULocationID` +7. For "Frequency unit" choose "Hourly" +8. For "Frequency quantity" put 1 +9. For "Method to aggregate numeric values" choose "mean" +10. Use default values for the rest of parameters + + + +11. Choose Preview +12. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all current steps and a preview of a resulted dataset. + + + +## Resample time series +Now we are ready to make a final aggregation! We aggregate all rows by combination of `PULocationID` and `pickup_time` timestamp while features should be replaced by mean value for each combination. + +We use special built-in Time Series transformation **Resample**. The Resample transformation changes the frequency of the time series observations to a specified granularity. It also comes with both upsampling and downsampling options. Applying upsampling increases the frequency of the observations, for example from daily to hourly, whereas downsampling decreases the frequency of the observations, for example from hourly to daily. + +To create this transformation you have to: +1. Click the plus sign next to a collection of transformation elements and choose Add transform. + + + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Time Series. + + + +4. For "Transform" choose "Resample" +5. For "Timestamp" choose pickup_time +6. For "ID column" choose "PULocationID" +7. For "Frequency unit" choose "Hourly" +8. For "Frequency quantity" put 1 +9. For "Method to aggregate numeric values" choose "mean" +10. Use default values for the rest of parameters + + + +11. Choose Preview +12. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all current steps until this point in time and get a preview of the resulting dataset. + + + +[here](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-import.html). + +## Featurize Date Time + +"Featurize datetime" time series transformation will add the month, day of the month, day of the year, week of the year, hour and quarter features to our dataset. Because we’re providing the date/time components as separate features, we enable ML algorithms to detect signals and patterns for improving prediction accuracy. + +To create this transformation you have to: +1. Click the plus sign next to a collection of transformation elements and choose Add transform + +2. Click "+ Add step" orange button in the TRANSFORMS menu + + + +3. Choose Time Series + + + + * For "Transform" choose "Featurize date/time" + * For "Input Column" choose `pickup_time` + * For "Output Column" enter "date" + * For "Output mode" choose "Ordinal" + * For "Output format" choose "Columns" + * For date/time features to extract, select Year, Month, Day, Hour, Week of year, Day of year, and Quarter. + + + +4. Choose Preview +5. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all current steps until this point in time and get a preview of the resulting dataset. + +Click "Back to data flow" to head back to the block diagram editor window. + +## Lag feature +Next let’s create lag features for the target column count. Lag features in time-series analysis are values at prior timestamps that are considered helpful in inferring future values. They also help identify **autocorrelation**, also known as serial correlation, patterns in the residual series by quantifying the relationship of the observation with observations at previous time steps. Autocorrelation is similar to regular correlation but between the values in a series and its past values. It forms the basis for the **autoregressive forecasting** models in the **ARIMA** series. + +With SageMaker Data Wrangler's Lag feature transform, you can easily create lag features n periods apart. Additionally, we often want to create multiple lag features at different lags and let the model decide the most meaningful features. For such a scenario, the **Lag features** transform helps create multiple lag columns over a specified window size. + +To create this transformation, follow the steps below: +1. Click the plus sign next to a collection of transformation elements and choose Add transform. + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Time Series + + + + * For "Transform" choose "Lag features" + * For "Generate lag features for this column" choose "count" + * For "ID column" enter "PULocationID" + * For "Timestamp Column" choose "pickup_time" + * For Lag, enter 8. You could try to use different values, maybe 24 hours in our case makes more sense. + * Because we’re interested in observing up to the previous 8 lag values, let’s select Include the entire lag window. + * To create a new column for each lag value, select Flatten the output + + + +4. Choose Preview +5. Choose Add to save the step. + +When transformation is applied on a sampled data you should see all current steps and a preview of a resulted dataset. + + + +## Rolling window features +We can also calculate meaningful statistical summaries across a range of values and include them as input features. Let’s extract common statistical time series features. + +Data Wrangler implements automatic time series feature extraction capabilities using the open source `tsfresh` package. With the time series feature extraction transforms, you can automate the feature extraction process. This eliminates the time and effort otherwise spent manually implementing signal processing libraries. We will extract features using the **Rolling window** features transform. This method computes statistical properties across a set of observations defined by the window size. + +To create this transformation you have to: +1. Click the plus sign next to a collection of transformation elements and choose Add transform + +2. Click "+ Add step" orange button in the TRANSFORMS menu. + + + +3. Choose Time Series + + + + * For "Transform" choose "Rolling window features" + * For "Generate rolling window features for this column" choose "count" + * For "Timestamp Column" choose "pickup_time" + * For "ID column" enter `PULocationID` + * For "Window size", enter 8. You could try to use different values, maybe 24 hours in our case makes more sense. + * Select Flatten to create a new column for each computed feature. + * Choose "Strategy" as "Minimal subset". This strategy extracts eight features that are useful in downstream analyses. Other strategies include Efficient Subset, Custom subset, and All features. + + + +4. Choose Preview +5. Choose Add to save the step. + +When this transformation is applied on the dataset, you can see all current steps until this point in time and get a preview of the resulting dataset. + + + +Click "Back to data flow" to head back to the block diagram editor window. + +## Export Data +At this stage, we have a new dataset that is cleaned and transformed with newly engineered features. This dataset can be used for forecasting either using open source libraries/frameworks or AWS services like [Amazon SageMaker Autopilot](https://aws.amazon.com/sagemaker/autopilot/), [Amazon SageMaker Canvas](https://aws.amazon.com/sagemaker/canvas/) or [Amazon Forecast](https://aws.amazon.com/forecast/). + +Given, we had only used a sample of the dataset for creating our data preparation and transformation recipe so far, what need to do next is to apply the same recipe (data flow) on our entire dataset and scale the whole process in a distributed fashion. Amazon Data Wrangler let's you do this in multiple ways. You can export your data flow: 1/ as a processing job, 2/ as a SageMaker pipeline step, or 3/ as a Python script. You can also kick-off these distributed jobs via the UI without writing any code using Data Wrangler's destination node option. The export options are also facilitated via SageMaker Studio notebooks (Jupyter). Additionally, the transformed features can also be ingested directly to SageMaker Feature Store. + +For this lab, we will see how to use the destination nodes option to export the transformed features to S3 via a distributed PySpark job powered by SageMaker Processing. + +### Exporting to S3 using Destination Nodes +This option creates a SageMaker processing job which uses the data flow (recipe) we have created previously to kick-off a distributed processing job on the "entire" dataset saving the results to a specified S3 bucket. + +Additionally, you can also drop columns if needed right before the export step. For the sake of brevity and to simplify the prediction problem statement, let's drop all columns except three columns `pickup_time`, `count`, `PULocationID`. Here count is the target variable we will try to predict. `pickup_time` and `PULocationID` will be our feature columns used for modeling. To create the model, we will be using SageMaker Autopilot. This will be covered in the next 2 sections. + +Follow the next steps to setup export to S3. +1. Click the plus sign next to a collection of transformation elements and choose **"Add destination" -> "Amazon S3"** + + + +2. Provide parameters for S3 destination: + * Dataset name - name for new dataset, for example used "NYC_export" + * File type - CSV + * Delimiter - Comma + * Compression - none + * Amazon S3 location - You can use the same bucket name which we created at the beginning + +3. Click "Add destination" orange button + + + +4. Now your dataflow has a final step and you see a new "Create job" orange button. Click it. + + + +5. Provide a "Job name" or keep autogenerated option and select "destination". We have only one "S3:NYC_export", but you might have multiple destinations from different steps in your workflow. Leave a "KMS key ARN" field empty and click "Next" orange button. + + + +6. Now your have to provide configuration for a compute capacity for a job. You can keep all defaults values: + * For Instance type use "ml.m5.4xlarge" + * For Instance count use "2" + * You can explore "Additional configuration", but keep them without change. + * Click "Run" orange button + + + +7. Now your job is started and it takes about 1 hour to process 6 GB of data according to our Data Wrangler processing flow. Cost for this job will be around 2 USD as "ml.m5.4xlarge" cost 0.922 USD per hour and we are using two of them. + + + +8. If you click on the job name you will be redirected to a new window with the job details. On the job details page you see all parameters from a previous steps. + + + +Approximately in one hour you should see that job status changed to "Completed" and you could also check "Processing time (seconds)" value. + + + +Now you could close job details page. + +## Check Processed output +After the SageMaker Data Wrangler processing job is completed, we can check the results saved in our destination S3 bucket. + +At this stage, you have designed a data flow for data processing and feature engineering and successfully launched it. Of course it is not mandatory to always run a job by clicking on the "Run" button. You could also automate it, but this is a topic of another workshop in this series! + +
💡 +Congratulations!
+ You reached the end of this part. Now you know how to use Amazon SageMaker Data Wrangler for time series dataset preparation! + + You can now move to an optional advanced time series transformation exercise +
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