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Use point aggregation methods within your line plots for improved data visualization accuracy and performance. There are two types of point aggregation modes: full fidelity and random sampling. W&B uses full fidelity mode by default.

Full fidelity

When you use full fidelity mode, W&B breaks the x-axis into dynamic buckets. The number of points per line adapts to chart size and the number of runs. It calculates the minimum and maximum values within each bucket (used for optional shading) and uses the last value in each bucket to draw the line. There are three main advantages to using full fidelity mode for point aggregation:
  • Preserve extreme values and spikes: retain extreme values and spikes in your data.
  • Configure how minimum and maximum points render: use the W&B App to interactively decide whether you want to show extreme (min/max) values as a shaded area.
  • Explore your data without losing data fidelity: W&B recalculates x-axis bucket sizes when you zoom into specific data points. This helps ensure that you can explore your data without losing accuracy. Caching is used to store previously computed aggregations to help reduce loading times which is particularly useful if you are navigating through large datasets.

Turn on full fidelity

W&B uses full fidelity mode by default. To configure it manually, follow these steps:
  1. Navigate to your workspace.
  2. Select the gear icon on the top right corner of the screen next to the left of the Add panels button.
  3. From the UI slider that appears, select Line plots
  4. Choose Full fidelity from the Point aggregation section.
  5. Configure the Smoothing algorithm and settings.
  6. Set Aggregation to Mean, Min, or Max.
  7. Click Apply.

Configure shading

The shaded areas of a full-fidelity line plot can show:
  • Min/Max: For each X-axis point, shade the area between the minimum and maximum values. The shaded area shows all points from the lowest to the highest value in each bucket: Min/Max Range=[min(x1,x2,,xn), max(x1,x2,,xn)]\text{Min/Max Range} = [\min(x_1, x_2, \ldots, x_n),\ \max(x_1, x_2, \ldots, x_n)] where x1,x2,,xnx_1, x_2, \ldots, x_n are the values in a given bucket.
  • Standard deviation: For each X-axis point, calculate the variability of the values using standard deviation, and shade the resulting area. SD=1ni=1n(xix)2SD = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i - \overline{x})^2}
  • Standard error: For each X-axis point, calculate the likelihood of a sampling error by dividing the value by the square root of the sample size: SE=SDnSE = \frac{SD}{\sqrt{n}}
  • None: No shading (the default).
The proceeding image shows a blue line plot. The light blue shaded area represents the minimum and maximum values for each bucket.
Shaded confidence areas
To configure shading:
  1. Navigate to your workspace.
  2. Hover over a line plot, then click the gear icon.
  3. In the Data tab, set Point aggregation to Full fidelity if necessary, then configure the smoothing algorithm.
  4. In the Grouping tab, turn on Group runs. Optionally, set Group by to a run attribute.
  5. Set Agg to Mean (default), Min, or Max.
  6. Set Range to Min/Max, Std Dev, Std Err, or None.
  7. Click Apply.

Explore your data without losing data fidelity

Analyze specific regions of the dataset without missing critical points like extreme values or spikes. When you zoom in on a line plot, W&B adjusts the bucket sizes used to calculate the minimum, maximum, and last value within each bucket.
Plot zoom functionality
W&B uses dynamic binning to divide the x-axis into buckets. The number of points shown per line adapts to chart size and the number of runs: smaller chart dimensions or more runs can reduce the number of points per line so that the chart remains responsive and can display more lines. For each bucket, W&B calculates the following values:
  • Minimum: The lowest value in that bucket (used for shading).
  • Maximum: The highest value in that bucket (used for shading).
  • Line value: The last value in that bucket, used to draw the line.
W&B plots values in buckets in a way that preserves full data representation and includes extreme values in every plot. The line is drawn from the last value in each bucket. When you zoom in far enough, full fidelity mode can render every data point without additional aggregation; the exact threshold depends on the current chart dimensions and number of runs. To zoom in on a line plot, follow these steps:
  1. Navigate to your W&B project
  2. Select on the Workspace icon on the left tab
  3. Optionally add a line plot panel to your workspace or navigate to an existing line plot panel.
  4. Click and drag to select a specific region to zoom in on.
Line plot grouping and expressionsWhen you use Line Plot Grouping, W&B applies the following based on the mode selected:
  • Non-windowed sampling (grouping): Aligns points across runs on the x-axis. The average is taken if multiple points share the same x-value; otherwise, they appear as discrete points.
  • Windowed sampling (grouping and expressions): Divides the x-axis either into 250 buckets or the number of points in the longest line (whichever is smaller). W&B takes an average of points within each bucket.
  • Full fidelity (grouping and expressions): Similar to non-windowed sampling, but fetches up to 500 points per run to balance performance and detail.

Random sampling

Random sampling uses 1500 randomly sampled points to render line plots. Because sampling drops points, it make spotting outliers or spikes more difficult.
Random sampling samples non-deterministically. This means that random sampling sometimes excludes important outliers or spikes in the data and therefore reduces data accuracy.

Enable random sampling

By default, W&B uses full fidelity mode. To enable random sampling, follow these steps:
  1. Navigate to your W&B project
  2. Select on the Workspace icon on the left tab
  3. Select the gear icon on the top right corner of the screen next to the left of the Add panels button.
  4. From the UI slider that appears, select Line plots
  5. Choose Random sampling from the Point aggregation section

Access non sampled data

You can access the complete history of metrics logged during a run using the W&B Run API. The following example demonstrates how to retrieve and process the loss values from a specific run:
# Initialize the W&B API
run = api.run("l2k2/examples-numpy-boston/i0wt6xua")

# Retrieve the history of the 'Loss' metric
history = run.scan_history(keys=["Loss"])

# Extract the loss values from the history
losses = [row["Loss"] for row in history]