Predictor -- Regression

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This is a Transform type Snap that enables you to predict the target field for an unlabeled document. An unlabeled document is one that does not have a label field. So, the Snap reads this unlabeled document and predicts the target field. Predictions are made based on the regression model built by the Trainer (Regression) Snap.

Input and Output

Expected input: An unlabeled document and the regression model. 

Expected output: Predictions from the regression model based on the input document. 

Expected upstream Snaps:

  • First input view: Any Snap that generates an unlabeled document document. For example, JSON Parser, JSON Generator, CSV Parser, CSV Generator, Mapper, and so on.
  • Second input view: Any Snap that reads and outputs the regression model. For example, a combination of File Reader, and JSON Parser.

Expected downstream Snaps: Any Snap that uses the predicted result. For example, Aggregate, or a combination of File Writer and JSON Formatter.


The input document must be in tabular format (no nested structure). 

Configuring Accounts

Accounts are not used with this Snap.

Configuring Views


This Snap has exactly two document input views. The first input view is for the unlabeled document that requires prediction. The second input view is for the regression model.
OutputThis Snap has exactly one document output view.
ErrorThis Snap has at most one document error view.



Limitations and Known Issues



Snap Settings

LabelRequired. The name for the Snap. Modify this to be more specific, especially if there are more than one of the same Snap in the pipeline.

Snap Execution

Select one of the three modes in which the Snap executes. Available options are:

  • Validate & Execute: Performs limited execution of the Snap, and generates a data preview during Pipeline validation. Subsequently, performs full execution of the Snap (unlimited records) during Pipeline runtime.
  • Execute only: Performs full execution of the Snap during Pipeline execution without generating preview data.
  • Disabled: Disables the Snap and all Snaps that are downstream from it.


Heating Load Prediction – Testing

The model trained in the Heating Load Prediction – Model Training example pipeline is tested against an unlabeled dataset. 

Download this pipeline.

To understand the dataset and the process prior to testing the model see the following examples:

 Understanding the pipeline

In this example, the unlabeled dataset is created using the CSV Generator Snap. This dataset is used as the data input for the Predictor (Regression) Snap. The target field (heating load) is to be predicted for this dataset. 

The model trained in the Heating Load Prediction – Model Training example is used as the model input. The File Reader Snap is configured to read this model from the SLDB. The JSON Parser Snap is used to parse the output from the File Reader Snap. Below is a preview of the output from the JSON Parser Snap (the model):

Model Testing

To test a model trained on a classification dataset, the Predictor (Regression) Snap must be used. 

The Predictor (Regression) Snap requires two inputs:

  • An unlabeled dataset for the data input
  • The ML model trained on the labeled dataset for the model input

The Predictor Snap is configured as shown below:

Based on its configuration, the output from the Snap includes one class prediction per document. This output is as shown below:

Download this pipeline.

Additional Example

The following use case demonstrates a real-world scenario for using this Snap:


Important steps to successfully reuse Pipelines

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

  File Modified

File Energy_Efficiency_Test_Model.slp

Nov 09, 2018 by Mohammed Iqbal

Snap Pack History

 Click to view/expand

4.29 (429patches16809)

  • Removed the log4j dependency from the ML Core Snaps due to security vulnerabilities.

4.29 (main15993)

  • Upgraded with the latest SnapLogic Platform release.

4.28 (main14627)

  • Upgraded with the latest SnapLogic Platform release.

4.27 (427patches13948)

4.27 (main12833)

  • No updates made.

4.26 (main11181)

  • No updates made.

4.25 (main9554)

  • No updates made.

4.24 (main8556)

  • No updates made.

4.23 (main7430)

  • No updates made.

4.22 (main6403)

  • No updates made.

4.21 (snapsmrc542)

  • No updates made.

4.20 Patch mlcore8770

  • Adds the log4j dependency to the ML Core Snaps to resolve the "Could not initialize class org.apache.log4j.LogManager" error. 

4.20 (snapsmrc535)

  • No updates made.

4.19 (snapsmrc528)

  • No updates made.

4.18 (snapsmrc523)

  • No updates made.

4.17 Patch ALL7402

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

4.17 (snapsmrc515)

  • New Snap: Introducing the Clustering Snap that performs exploratory data analysis to find hidden patterns or groupings in data.
  • Enhanced the AutoML Snap. You can now:
    • Select algorithms to derive the top models.
    • Input the best model generated by another AutoML Snap from a previous execution.
    • View an interactive HTML report that contains statistics of up to 10 models.
  • Added the Snap Execution field to all Standard-mode Snaps. In some Snaps, this field replaces the existing Execute during preview check box.

4.16 (snapsmrc508)

  • New Snap: Introducing the AutoML Snap, which lets you automate the process of selecting machine learning algorithms and tuning hyperparameters. This Snap gives the best predictive model within the specified time limit.

4.15 (snapsmrc500)

  • New Snap Pack. Perform data modeling operations such as model training, cross-validation, and model-based predictions. Additionally, you can also execute Python scripts remotely. Snaps in this Snap Pack are: 
    • Cross Validator -- Classification
    • Cross Validator -- Regression
    • Predictor -- Classification
    • Predictor -- Regression
    • Remote Python Script
    • Trainer -- Classification
    • Trainer -- Regression
  • Releases the Remote Python Executor account and the Remote Python Executor Dynamic account for the Remote Python Script Snap.