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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)

SnapLogic Documentation > Feature Synthesis > image2019-4-1_19-23-35.png

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.
Output	This Snap has exactly one document output view.
Error	This Snap has at most one document error view.

Troubleshooting

None.

Limitations and Known Issues

None.

Modes

Ultra Pipelines: Does not work in Ultra Pipelines.

Snap Settings

Label

Required. 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.

Policy

Specify 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.

SnapLogic Documentation > Feature Synthesis > Pipeline.png

Download this 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:

SnapLogic Documentation > Feature Synthesis > Customer_Data_Base_Dataset.png

SnapLogic Documentation > Feature Synthesis > Transaction_Data_Reference_Dataset.png

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:

SnapLogic Documentation > Feature Synthesis > Feature_Synthesis_Snap_Configuration.png

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:

SnapLogic Documentation > Feature Synthesis > Feature_Synthesis_Snap_Output.png