Feature Synthesis

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Overview

The Feature Synthesis Snap generates new features for a base or primary dataset by joining it with other datasets (reference datasets) linked together by common identifiers. Features are measurements of data points in a dataset. Using this Snap enables you to view statistical data of all related datasets in the base dataset itself. Categorical and numeric data will get different set of features. Some of the features generated are:

  • Mean (Numeric data)
  • Min (Numeric data)
  • Max (Numeric data)
  • Mode (Categorical data)
  • Unique (Categorical data)
  • Count (Generated at the base dataset level)


Input and Output

Expected input

  • First input: The base dataset.
  • Subsequent input(s): The reference dataset(s).

Expected output: The base dataset containing all the features generated based on the reference datasets. 

Expected upstream Snaps: Snaps that offer a document output. For example, MySQL - Select, or PostgreSQL - Select.

Expected downstream Snaps: A Snap that accepts documents. For example, Mapper, JSON Formatter, or AutoML.

Prerequisites

The base dataset must have one-to-many or one-to-one relationship with the reference dataset; and the reference datasets must have one-to-one or one-to-many relationship with each other.

Configuring Accounts

Accounts are not used with this Snap.

Configuring Views

Input

This Snap has at least two document input views.
OutputThis Snap has exactly one document output view.
ErrorThis Snap has at most one document error view.

Troubleshooting

None.

Limitations and Known Issues

None.

Modes

Snap Settings

LabelRequired. The name for the Snap. You can modify this to be specific, especially if you have more than one of the same Snap in your pipeline.
PolicySpecify the base and reference datasets and fields.
Base view

The input view to which the base dataset is connected.

Default value: input0 (name of the input view)

Base path

The field in the base dataset that is to be used as the base field. This has to be the common identifier for joining with the reference dataset.

Example: $customer_id

Default value: [None]

Ref view

View where the reference dataset is the input.

Default value: input0 (name of the input view)

Ref path

The field in the reference dataset that is to be used. This field must have the same values as in the base field.

Example: $customer_id

Default value: [None]

  Snap Execution

Specifies the execution type:

  • Validate & Execute: Performs limited execution of the Snap (up to 50 records) during Pipeline validation; performs full execution of the Snap (unlimited records) during Pipeline execution.
  • Execute only: Performs full execution of the Snap during Pipeline execution; does not execute the Snap during Pipeline validation.
  • Disabled: Disables the Snap and, by extension, its downstream Snaps.

Default value: Validate & Execute

Examples

Getting Customer Insights Based on Transaction Data

This example shows how the Feature Synthesis Snap is used to generate features based on transaction dataset and added to customer dataset.

Download this Pipeline.

 Understanding the Pipeline

The base dataset in this example is a collection of customer records. It has the following fields:

  • $customer_id
  • $firstname
  • $lastname
  • $create_time

The reference dataset is a collection of transactions made by the customers listed in the base dataset. It has the following fields:

  • $transaction_id
  • $customer_id
  • $source
  • $num_item
  • $total 

Both datasets are provided by the CSV Generator Snaps titled Customers and Transactions. These are passed through a Type Converter Snaps so that all data types are mapped correctly. This is required to enable the Feature Synthesis Snap to generate features accurately. 

A preview of the customer and transaction datasets that are output by the CSV Generator Snaps is as shown below:

 

The field $customer_id is common between both datasets. The Feature Synthesis Snap will use this field to join datasets and is configured as shown below:

 

The customer dataset is connected to the first input view (titled customer); so this view becomes the Base view. Similarly, the transaction dataset is connected to the second input view (titled transaction); so that becomes the Ref view.

Upon successful execution, the Feature Synthesis Snap generates features and adds them to the base dataset as shown below: 

The same output is shown in a JSON format to let you see the full list of features:

Download this Pipeline.

Downloads

Important steps to successfully reuse Pipelines

  1. Download and import the pipeline into the SnapLogic application.
  2. Configure Snap accounts as applicable.
  3. Provide pipeline parameters as applicable.

  File Modified

File Feature_Synthesis.slp

Apr 19, 2019 by Mohammed Iqbal
 

Snap Pack History

 Click to view/expand
Release Snap Pack VersionDateType  Updates
November 2024main29029 StableUpdated and certified against the current SnapLogic Platform release.

August 2024

main27765

 

Stable

  • Upgraded the org.json.json library from v20090211 to v20240303, which is fully backward compatible.
  • Enhanced the Date Time Extractor Snap to support Date time formats (YYYY-MM-dd HH:mm:ss and YYYY-MM-dd HH:mm:ss.SSS) and allow the root path to auto-convert all fields.

May 2024main26341 StableUpdated and certified against the current SnapLogic Platform release.
February 2024436patches25781 Latest

Enhanced the Deduplicate Snap to honor an interrupt while waiting in the delay loop to manage the memory efficiently.

February 2024main25112 StableUpdated and certified against the current SnapLogic Platform release.
November 2023main23721Nov 8, 2023StableUpdated and certified against the current SnapLogic Platform release.

August 2023

main22460

Aug 16, 2023

Stable

Updated and certified against the current SnapLogic Platform release.

May 2023433patches21572 Latest

The Deduplicate Snap now manages memory efficiently and eliminates out-of-memory crashes using the following fields:

  • Minimum memory (MB)

  • Minimum free disk space (MB)

May 2023433patches21247 Latest

Fixed an issue with the Match Snap where a null pointer exception was thrown when the second input view had fewer records than the first.

May 2023

main21015 

Stable

Upgraded with the latest SnapLogic Platform release.

February 2023main19844 StableUpgraded with the latest SnapLogic Platform release.
December 2022431patches19268 Latest

The Deduplicate Snap now ignores fields with empty strings and whitespaces as no data.

November 2022main18944
 
Stable

Upgraded with the latest SnapLogic Platform release.

August 2022main17386
 
Stable

Upgraded with the latest SnapLogic Platform release.

4.29main15993
 
StableUpgraded with the latest SnapLogic Platform release.
4.28main14627
 
Stable

Enhanced the Type Converter Snap with the Fail safe upon execution checkbox. Select this checkbox to enable the Snap to convert data with valid data types, while ignoring invalid data types.

4.27427patches13730

Enhanced the Type Converter Snap with the Fail safe upon execution checkbox. Select this checkbox to enable the Snap to ignore invalid data types and convert data with valid data types.

4.27427patches13948
 
Latest

Fixed an issue with the Principal Component Analysis Snap, where a deadlock occurred when data is loaded from both the input views.

4.27main12833
 
StableUpgraded with the latest SnapLogic Platform release.
4.26main11181
 
StableUpgraded with the latest SnapLogic Platform release.
4.25425patches10994
 

Fixed an issue when the Deduplicate Snap where the Snap breaks when running on a locale that does not format decimals with Period (.) character. 

4.25main9554
 
StableUpgraded with the latest SnapLogic Platform release.
4.24main8556
 
StableUpgraded with the latest SnapLogic Platform release.
4.23main7430
 
StableUpgraded with the latest SnapLogic Platform release.
4.22main6403
 
StableUpgraded with the latest SnapLogic Platform release.
4.21snapsmrc542
 
Stable
  • Introduces the Mask Snap that enables you to hide sensitive information in your dataset before exporting the dataset for analytics or writing the dataset to a target file.
  • Enhances the Match Snap to add a new field, Match all, which matches one record from the first input with multiple records in the second input. Also, enhances the Comparator field in the Snap by adding one more option, Exact, which identifies and classifies a match as either an exact match or not a match at all.
  • Enhances the Deduplicate Snap to add a new field, Group ID, which includes the Group ID for each record in the output. Also, enhances the Comparator field in the Snap by adding one more option, Exact, which identifies and classifies a match as either an exact match or not a match at all.
  • Enhances the Sample Snap by adding a second output view which displays data that is not in the first output. Also, a new algorithm type, Linear Split, which enables you to split the dataset based on the pass-through percentage.
4.20 Patchmldatapreparation8771
 
Latest

Removes the unused jcc-optional dependency from the ML Data Preparation Snap Pack.

4.20snapsmrc535
 
StableUpgraded with the latest SnapLogic Platform release.
4.19snapsmrc528
 
Stable

New Snap: Introducing the Deduplicate Snap. Use this Snap to remove duplicate records from input documents. When you use multiple matching criteria to deduplicate your data, it is evaluated using each criterion separately, and then aggregated to give the final result.

4.18snapsmrc523
 
StableUpgraded with the latest SnapLogic Platform release.
4.17 PatchALL7402
 
Latest

Pushed automatic rebuild of the latest version of each Snap Pack to SnapLogic UAT and Elastic servers.

4.17snapsmrc515
 
Latest
  • New Snap: Introducing the Feature Synthesis Snap, which automatically creates features out of multiple datasets that share a one-to-one or one-to-many relationship with each other.
  • New Snap: Introducing the Match Snap, which enables you to automatically identify matched records across datasets that do not have a common key field.
  • Added the Snap Execution field to all Standard-mode Snaps. In some Snaps, this field replaces the existing Execute during preview check box.
4.16snapsmrc508
 
Stable

Added a new Snap, Principal Component Analysis, which enables you to perform principal component analysis (PCA) on numeric fields (columns) to reduce dimensions of the dataset.

4.15snapsmrc500
 
Stable
  • New Snap Pack. Perform preparatory operations on datasets such as data type transformation, data cleanup, sampling, shuffling, and scaling. Snaps in this Snap Pack are: 
    • Categorical to Numeric
    • Clean Missing Values
    • Date Time Extractor
    • Numeric to Categorical
    • Sample
    • Scale
    • Shuffle
    • Type Converter