Embedding Visualization With UMAP
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Embedding visualization is a powerful technique for understanding and interpreting complex relationships in high-dimensional data. Reducing the dimensionality of custom features into a 2D or 3D space makes identifying patterns, clusters, and outliers easier.
In Fiddler, high-dimensional data like embeddings and vectors are ingested as a .
Our goal in this document is to visualize these custom features.
To create an embedding visualization chart, follow these steps:
Navigate to the Charts tab in your Fiddler AI instance
Click on the Add Chart button on the top right
In the modal, select the project that has a model with Custom features
Select Embedding Visualization.
When creating an embedding visualization chart, you will need to specify the following parameters:
Model and model version
Embedding column
Display columns
Baseline
Segment
Date range
Sample size
Advanced fields
Number of neighbors
Minimum distance
Distance metric
Please see below for details on these parameters.
Select the model containing at least one embedding column. You may further refine to a model version if required.
Choose the embedding column from your dataset that you wish to visualize.
Select the columns for which you want to display additional information when hovering over points in the visualization. When plot points are selected, these additional display columns will also be available in the data cards.
Select a baseline for comparison. This is optional and will be helpful when comparing datasets, such as a pre-production dataset with a production dataset or two time periods in production.
Select an existing segment (or define a new segment) to filter the chart to a particular data cohort. This is optional, but it will be helpful when focusing on a specific cohort.
Decide the number of samples you want to include for performance and clarity in the visualization. Currently, sample sizes between 100 and 10,000 can be selected. In future releases, we will enable support for larger sample sizes.
This parameter controls how UMAP balances local versus global structure in the data. It determines the number of neighboring points used in the manifold approximation. Low values of this parameter, such as 5, will lead UMAP to focus too much on the local structure, losing sight of the big picture. Conversely, bigger values will lead to a focus on the broader data. It is important to experiment on your dataset and use case to identify the value that provides the best results. Values from 2 to 100 are supported.
Controls how closely points can be placed to each other in the visualization. A smaller value (such as 0.1) allows points to cluster more tightly, revealing finer details and local structures in your data. A larger value forces points to spread out more evenly across the visualization space.
When generating the embedding visualization, you can choose different periods of production data to analyze. To do this:
Access the Date Range selector.
Choose the start and end dates for the period you are interested in.
The visualization will update to reflect the embeddings from the selected date range.
The 'Color By' feature enriches the visualization by categorizing your data points using different colors based on attributes.
Find the 'Color By' dropdown in your control panel.
Choose a categorical feature to color-code the data points. For example, select "data source" to color the data points according to whether they are baseline or production data.
Using the 'Color By' feature can help uncover patterns in your data. For instance, in the above image, data points with varying 'target' column values demonstrate clustering, where similar values tend to group.
You can also select points to delve deeper for further inspection. This ability to interactively color and select data points may be very useful for root cause analysis.
Zooming in on the UMAP chart provides a closer look at clusters and individual data points.
Use the mouse scroll wheel to zoom in or out.
Click and drag the mouse to move the zoomed-in area around the chart.
Zooming helps to focus on areas of interest or to distinguish between closely packed points.
You can select individual or groups of data points to analyze further.
Click on a data point to select it. Or use the Selector on the top right to select multiple points
Selected points will be highlighted on the chart, and details of the display columns of these cards are displayed in data cards, as shown below
Use this feature to identify and analyze specific data points
In the following example, we use the categorical attribute "feedback", which contains three possible values: like, dislike, or None, as the legend indicates. After applying the 'color by' feature, the user selects specific data points to examine in greater detail. The selected data points are then presented as data cards below.
Hovering over a data point reveals additional information about it, providing immediate insight without the need for selection.
Move the cursor over a data point on the chart
A tooltip will appear, displaying the data associated with that point, such as values of different display columns
Use this feature to quickly look up data without altering your current selection on the chart
Once you're satisfied with your visualization, you can save the chart. This chart can then be added to a dashboard. This allows you to revisit the UMAP visualization at any time easily, either directly from the Chart or from the dashboard.
We use the (Uniform Manifold Approximation and Projection) technique for embedding visualizations. UMAP is a dimension reduction technique that is particularly good at preserving the local structure of the data, making it ideal for visualizing embeddings. We reduce the high-dimensional embeddings to a 3D space.
UMAP is supported for both Text and Image embeddings using a
Questions? to a product expert or a demo.
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