Embedding Visualization With UMAP
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Embedding visualization is a powerful technique used to understand and interpret complex relationships in high-dimensional data. Reducing the dimensionality of custom features into a 2D or 3D space makes it easier to identify patterns, clusters, and outliers.
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
Select Embedding Visualization.
When creating an embedding visualization chart, you will need to specify the following parameters:
Model
Custom Feature Column
Baseline
Display Columns
Sample size
Number of Neighbors
Minimum distance
Please see below for details on these parameters.
Select the model (with custom features) for which you want to visualize the embeddings.
Choose the custom feature column from your dataset that you wish to visualize.
Define a baseline for comparison. This is optional and will be useful when you want to compare datasets such as a pre-production dataset with a production dataset or two time periods in production.
Select the columns that you want to display additional information for when hovering over points in the visualization. These additional display columns will also be available in the data cards when points are selected.
Decide on the number of samples you want to include in the visualization for performance and clarity. Currently, a sample size of either 100, 500, or 1000 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 (for example: 5) of this parameter will lead UMAP to focus too much on the local structure losing sight of the big picture, alternatively, bigger values will lead to a focus on the broader data. It is important to experiment on your dataset and use case to identify a value that works best for you.
Set the minimum distance apart that points are allowed to be in the low-dimensional representation. Smaller values (for example: 0.1) will result in a more clustered embedding, which can highlight finer details.
When generating the embedding visualization, you have the flexibility to choose different time periods of production data to analyze. To do this:
Access the time period selector.
Choose the start and end dates for the time period you are interested in.
The visualization will update to reflect the embeddings from the selected time 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 instance, in the above image, the data points are assigned colors based on a 'target' categorical column. This attribute includes categories like Sandal, Trouser, and Pullover, as indicated in the legend.
Using the 'Color By' feature can assist in uncovering 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 together.
You can also select points to delve deeper for further inspection. You may find this ability to interactively color and select data points 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 Null, as indicated in the legend. 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 for a quick look-up of 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 going to the Chart or to the dashboard
We utilize 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 in .
In the modal, Select the project that has a with Custom features
Questions? to a product expert or a demo.
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