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halerium 3.3.1 documentation
halerium 3.3.1 documentation
  • Getting Started
    • Installation
    • High-level usage - Examples
      • Causal Structures - Creation & Training
      • Causal Structures - Prediction
      • Objectives - Introduction
      • Causal Structures - Intervention-Prediction
      • Performance Evaluation
      • Outlier Detection
      • Influence Estimation
      • Rank Estimation
      • Probability Estimation
      • Causal Inference Example - Coupon Case
      • Causal Structures - applied on the California School data set
      • Causal Structures and Dependencies
    • High-level usage - Code Overview
      • Causal Structures - Creation & Training
      • Causal Structures - Prediction
      • Objectives - Introduction
      • Performance Evaluation
      • Outlier Detection
      • Influence Estimation
      • Rank Estimation
      • Probability Estimation
    • Building blocks of halerium models
    • Low-level usage - Examples
      • Building blocks of halerium models
      • Example: Inheritance and body heights
      • Simple training with the Trainer
      • More on training models
      • What happens during model creation and training
      • Distributions
      • Regression
      • Gaussian Process Regression
      • Logistic regression
      • Copying, templating and serialization
      • Estimate probabilities with the Probability Estimator
      • Estimate influences with the Influence Estimator
      • Make predictions with the Predictor
      • Evaluate the quality of a model with the Evaluator
      • Estimate ranks with the RankEstimator
      • Detecting outliers with the OutlierDetector
      • Causal Models - the Basics
      • Creating MA, AR, and ARMA Graphs
      • Applying an SARIMA model to the DutchSales data
      • Full densities instead of point predictions
      • Model uncertainty propagation
      • Training with missing data
      • Encoding causal structure
      • Reusing models
    • Low-level usage - Code Overview
      • Scoping Details
      • Displaying Graphs
      • Details about links
      • More on training models
      • Simple training with the Trainer
      • Estimate probabilities with the Probability Estimator
      • Estimate influences with the Influence Estimator
      • Make predictions with the Predictor
      • Evaluate the quality of a model with the Evaluator
      • Estimate ranks with the RankEstimator
      • Detecting outliers with the OutlierDetector
      • Distributions
      • Regression
      • Gaussian Process Regression
      • Logistic regression
      • Causal Models - the Basics
      • Creating MA, AR, and ARMA Graphs
      • Applying an SARIMA model to the DutchSales data
  • High-level usage - Examples
    • Causal Structures - Creation & Training
    • Causal Structures - Prediction
    • Objectives - Introduction
    • Causal Structures - Intervention-Prediction
    • Performance Evaluation
    • Outlier Detection
    • Influence Estimation
    • Rank Estimation
    • Probability Estimation
    • Causal Inference Example - Coupon Case
    • Causal Structures - applied on the California School data set
    • Causal Structures and Dependencies
  • High-level usage - Code Overview
    • Causal Structures - Creation & Training
    • Causal Structures - Prediction
    • Objectives - Introduction
    • Performance Evaluation
    • Outlier Detection
    • Influence Estimation
    • Rank Estimation
    • Probability Estimation
  • Low-level usage - Examples
    • Building blocks of halerium models
    • Example: Inheritance and body heights
    • Simple training with the Trainer
    • More on training models
    • What happens during model creation and training
    • Distributions
    • Regression
    • Gaussian Process Regression
    • Logistic regression
    • Copying, templating and serialization
    • Estimate probabilities with the Probability Estimator
    • Estimate influences with the Influence Estimator
    • Make predictions with the Predictor
    • Evaluate the quality of a model with the Evaluator
    • Estimate ranks with the RankEstimator
    • Detecting outliers with the OutlierDetector
    • Causal Models - the Basics
    • Creating MA, AR, and ARMA Graphs
    • Applying an SARIMA model to the DutchSales data
    • Full densities instead of point predictions
    • Model uncertainty propagation
    • Training with missing data
    • Encoding causal structure
    • Reusing models
  • Low-level usage - Code Overview
    • Scoping Details
    • Displaying Graphs
    • Details about links
    • More on training models
    • Simple training with the Trainer
    • Estimate probabilities with the Probability Estimator
    • Estimate influences with the Influence Estimator
    • Make predictions with the Predictor
    • Evaluate the quality of a model with the Evaluator
    • Estimate ranks with the RankEstimator
    • Detecting outliers with the OutlierDetector
    • Distributions
    • Regression
    • Gaussian Process Regression
    • Logistic regression
    • Causal Models - the Basics
    • Creating MA, AR, and ARMA Graphs
    • Applying an SARIMA model to the DutchSales data
  • Release Notes
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The link function#

Aliases#

halerium.core.link
halerium.core.graph.link
link(source, target, name=None)#

Establish link.

Establish a link between the source and the target. Typically this is used to designate the output of one sub-graph as the input of another sub-graph, which will connect the two graphs into a longer chain of operations.

The target will effectively be set to be equal to the source. All variables in a target refer to their counterparts in the source, when evaluated.

The source and the target have to meet certain requirements for the link to be accepted: 1. All children of the target must have a counterpart with

the same relative name in the source. For variables, the shape and type (static or dynamic) must also be equal.

  1. The target and its children must not be the target of an existing link.

  2. If the target is not in a graph input, the link must be called from the target’s parent graph.

  3. If the target is in a graph input, the link must be called from the parent graph of the targets parent graph instead.

  4. If the source is not in a graph output, the link must be called from the source’s parent graph.

  5. If the source is ina graph output, the link must be called from the parent graph of the source’s parent graph instead.

Parameters:

  • source (LinkableScopetor) – The source.

  • target (LinkableScopetor) – The target.

  • name (str, optional) – The name of the Link instance. If None it is chosen automatically. The default is None.

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Contents
  • The link function
    • Aliases