Dimension Reduction in Acadia

Overview

Dimension reduction is a fundamental technique in data processing that reduces the number of random variables under consideration, by obtaining a set of principal variables. It is crucial for simplifying models, speeding up computations, and helping to avoid the curse of dimensionality. In Acadia, dimension reduction models are used primarily in the context of data visualization and embedding processes.

Importance of Dimension Reduction

Dimension reduction can help in several ways:

Improving Model Performance: Reduces the complexity of data, which can improve the performance of machine learning algorithms.
Data Visualization: Helps in visualizing high-dimensional data in a 2D or 3D space, making it easier to perceive patterns and insights.
Noise Reduction: Helps in removing noise from data, which can enhance the accuracy of the models.

Using Dimension Reduction in Acadia

To use dimension reduction in Acadia, users can define a dimension reduction model that specifies how the dimensional reduction should be applied to the data. Acadia provides a framework for creating both standard and custom dimension reduction models.

Creating a Dimension Reduction Model

You can create a dimension reduction model by specifying the number of dimensions to reduce the data to, and other parameters that control the behavior of the model. Here is an example using a mock dimension reduction model provided by Acadia:

dim_reduction_model = MockDimensionReductionModel(
    dims=2,  # Target number of dimensions
    normalize=True,  # Whether to normalize data after reduction
    hyperparameter_1=0.5,  # Example hyperparameter
    hyperparameter_2=0.5  # Another example hyperparameter
)

Applying Dimension Reduction

Once a dimension reduction model is defined, it can be used to transform data, typically during the data embedding process for either datums or tags by specifying which dimension reduction model to use in the embedding model parameters.

 
    embedding_model = MockDatumEmbeddingModel(
        task_context="This is some context to embed the image with",
        columns_to_embed={
            "caption_0": "text",
            "caption_1": "text",
            "image_0": "image",
        },
        dim_reduction_model=dim_reduction_model,
    )
 
 
    tag_embedding_model = MockTagEmbeddingModel(
        task_context="This is some context to embed the image with",
        tag_content_to_embed=["value"],
        dim_reduction_model=dim_reduction_model,
    )

Creating Custom Tagging Models Creating Custom Dimension Reduction Models