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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 that are generated 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. This is particularly useful in understanding a customer's spending behavior and other such scenarios. Generated features include:
- Mean
- Min
- Max
- Mode
- Unique
- Count
Input and Output
Expected input
- First input: The base dataset.
- Subsequent input(s): The reference dataset(s).
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Expected upstream Snaps: Snaps that offers 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. Or ; or 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. |
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| 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.
- Spark mode: Does not work in Spark mode.
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. |
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| Policy | Specify the base and reference datasets and fields. |
| Base view | View where The input view to which the base dataset is the inputconnected. 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(s). Example: $customer_id Default value: [None] |
| Ref view | View where the reference dataset is the input. Default value: input1 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 | TBA once this field's functionality is finalized. |
Examples
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Getting Customer Insights Based on Transaction Data
This example shows how the Feature Synthesis Snap is used to generate features using a base based on transaction dataset and a reference added to customer dataset.
Download this Pipeline.
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The base dataset in this example is a collection of customer 's records. It has the following fields:
The reference dataset is a collection of transactions made by the customers listed in the base dataset. It has the following fields:
Both of the above datasets are provided as CSV files by the CSV Generator Snaps titled Customers and Transactions. These are passed through a Type Converter Snaps so that all datatypes 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 generate features and is configured as shown below: The customer dataset is connected to the first input view (titled customer so ); 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
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Additional Resources
Snap History
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